Archive for category Methodology analysis

Appendix A: Data Gathering Site Specific Methodologies (draft 1)

Posted by Laura on Wednesday, 24 November, 2010

And just to mix it up today and support the idea that I actually do write things, this would be a draft of some methodologies that will be headed into my appendix. A lot of it feels really simple, already gets stated inside existing chapters as I use the data and would eat up word count in my methodology in a really unhealthy way. (I’ve got about 20 of these methodologies to do. I should really do a summary about each particular site I’m using too. We’ll see. This section first.)


Appendix A
Data Gathering Site Specific Methodologies

43 Things
User information
To get user information from 43 Things, the first step was to identify goals that related to an Australian or New Zealand based sport club.  Searching using various keywords, reading the goals that related to the keywords and determining if they related to the search accomplished this.  Once a goal was identified and people were identified as having completed the goal or intending to complete the goal, the goal was recorded on a row in a 43 Things specific spreadsheet.  In a separate column, the team and league to which the goal related to were also recorded.  After this was completed, the user pages for people with that goal were visited.  Their username, city, state, country, birthday, website, and date joined were all recorded.  The last notation to the row was to include the date that this information was gathered.
During the time that this data was gathered, 43 Things changed the information that was available on the profiles.  This was done sometime between early June 2010 and  early November 2010.  Subsequently information such as city, state, country, birthday, website were not available on user profiles.  Only data gathered prior to this time exists except in cases where the data was checked at a later date and the user’s information had previously been recorded.

Total user information
Attempts were made to benchmark the level of interest in a team by recording the number of relevant search results on 43 things.  This was done by documenting the league and team that were being searched for in a row.  After that, the keyword used for the search was recorded in the same row.  The search was then completed and the total results were recorded.  A textual analysis of the search results was conducted and the total number of relevant results was recorded.  Of those relevant results, the total number of people working to accomplish them was then recorded.  Finally, the date the search was conducted was recorded.

Alexa
Site rankings
A list of websites related to Australian and New Zealand sport was created.  This was recorded on a spreadsheet, with columns that listed the league and team that the domain featured.  For every domain on the list, the page about the domain on Alexa was checked.  The Alexa page URL for the domain was also recorded on the relevant row.  When visiting the page, the world rank was recorded.  If an Australian rank was also available, it was recorded in a separate Australian specific column.  Next, the date that this information was gathered was recorded.  After that, any notes the author had regarding the site were recorded.  This was mostly to identify the type of domain or if it ranked in a country outside Australia.  Lastly, in some cases, the paragraph of information provided by Alexa regarding the site’s traffic and demographics was recorded.

Bebo
User  information
Profile information from bebo users was gathered by running a search related to a specific team or league.  The league and team that the search was related to was documented.  Once this was done, the people search results were copy and pasted to Notepad.  The search results were then formatted for pasting to the bebo user spreadsheet. Once copy and pasted, the author attempted to convert user-inputted locations into real locations of city, state, country.  The location field results were then found in columns for city, state, country instead of a location column.  The columns that existed then were league, team, name, gender, age, city, state, and country.  A final column was added that recorded the date this data was gathered.

Videos, Groups, Band information
There are three different search tabs on bebo beyond people that have information about the community size and audience for Australian and New Zealand sport.  They are Video, Groups and Bands.  Searched related to a specific team, player or league were run.  The relationship between the searches and the league and club were recorded.  The search results were then copy and pasted to Notepad where the results were formatted so they could be pasted on to a separate bebo related spreadsheet.  Once this was done, the following headers where information could be found included type, total (fans/viewers/members), loves, profile views, group created, genre, city, state, country, uploaded, uploader, and description.   The city, state and country information was documented using the same methodology as the bebo profile information.  Finally, a column was added that included the data that this data was gathered on.

Total search results
Total search results data came by recording the search term used, and recording the team and league that connect to that search term.  Once that was done, the total results were recorded for People, Video, Music, Groups, Apps and Skins. The date the search was conducted was then recorded.  Finally, any notes regarding the search or its results were recorded.
When recording the results, in almost all cases, the total results were included.  In a few select cases, generally when the results were 20 or less, the total number of results deemed relevant were recorded.  This was deemed important for smaller sport fan communities where one or two videos or groups may represent the whole community.  An example search where this was done involved a search for “Giants Football Club” because the results picked up rugby league teams and the New York Giants.

BlackPlanet
User information
User information was gathered once a search had been conducted and resulted in a user appearing.  If a user appeared for that search, the league and team related to that search were recorded.  The user name was then recorded.  The user page was then opened and the following data was collected: Country, gender and age. Lastly, the date this information was gathered was documented.

Total user information
To gather total user information, a search phrase was thought of and recorded.  In separate columns, the league and team the search related to were recorded.  The profile search was then conducted and the total number of results were recorded.  Finally, the date the search was conducted on was recorded.

Blogger
User information
User information was gathered once a search had been conducted and resulted in a user appearing.  If a user appeared for that search, the league and team related to that search were recorded.  The user name was then recorded.  The user page was then opened and the following data was collected: Age, Gender, Astrological sign, City, State, Country. Lastly, the date this information was gathered was documented.

Total user information
To gather total user information, a search phrase was thought of and recorded.  In separate columns, the league and team the search related to were recorded.  The profile search was then conducted and the total number of results were recorded.  Finally, the date the search was conducted on was recorded.

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Where do followers of Australian sport on Twitter come from?

Posted by Laura on Wednesday, 20 October, 2010

For the past two, almost three months, I’ve been focused on Twitter to the exclusion of almost all other social networks. This is because of the size of the Twitter audience and because of the complexities of trying to determine the location of those Twitter followers. I’d really like to move on but I’ve ended up in a perfectionist loop where I can’t seem to find the command for breaking it.

My over riding goal of sorts, in regards to Australian sport on Twitter, is to determine the size and location of the Australian sport community on Twitter as expressed by following Australian sport athletes, clubs, leagues and organizations. The methodology is relatively simple: 1) Develop a list of the major Australian sport related Twitter accounts. 2) Get a list of all the followers for those accounts. The list should include as much profile information as possible, including the user inputted location and time zone. 3) Translate user inputted locations into actual locations. When no user inputted location exists, attempt to use the time zone field to get this location.

The first step is a manual step. I’ve got to look around at other people’s lists, check follow lists and develop that list. My current list is around 450. It is constantly in a state of flux as people and organizations occassionally delete their accounts or get new ones with new names. (This leads to massive drop offs that I can’t always explain or notice in the moment. Sources to document the disappearance of accounts don’t always exist. Asking a month or two after the fact means that people can’t always recall what happened.)

The second step is automated. I’ve had a friend (@Hawkeye7) develop a tool that pulls that information for me. It is relatively simple once executed but its success largely depends on the input from step one. From October 14 to October 18, we got the follower list for every single account on that list of 450 people. It took four days because there are API limits on Twitter. We’d run across them, wait an hour and then get the next page of followers or next person’s followers. In the case of @warne888, this took at least 8 hours.

The third step relies both on manual input and automation. I’ve spent probably in the neighborhood of 60 hours working on creating a list of about 85,000 variants of user inputted locations. The tasks for doing this included reverse geocoding, creating lists of city/state/country patterns for Australia based on observations for patterns that I saw as repeating, individually evaluating user inputted locations to try to make an accurate guess as to what the user meant in terms of their location. My focus was on getting as much of the city/state/country information for Australia, New Zealand, Canada, the United States and Ireland. For countries other than those listed, I just tried to identify that the account came from a country. The selection of countries for most specific location data was based on where I saw Australian sport followers as coming from and my own understanding of a country’s geography. I don’t understand the geography of South Africa and Costa Rice as much as I could. I didn’t want to spend the time to completely understand it to better label those accounts. The time to overcome my learning curve felt better used by continuing to improve the list of unknowns to relatively known. Despite the huge amount of time spent on this, I have a list of over 50,000 user inputted locations that I haven’t begun to look at. As a perfectionist, this drives me absolutely nutters. I can’t possibly translate every user inputted location into a usable location. (I’m still working on improving it anyway.) The more accurate this list, the better my results. (And given the size of the data sets involved, I have my own user input errors that I don’t always find until I run the locations.)

To give this perspective, let’s look at @warne888. He had over 198,000 followers. I found over 50,000 user inputted locations which did not have a location I had appended to that variant. This, when it was pulling from a list of 65,000 variants. India turned out to be the big problem in that I didn’t have that many variants for cities in the country. Out of a list of 50,000 followers for @warne888, 17,000 of those followers listed neither a user inputted location nor a useable time zone for determining their location. Roughly 34% of his followers I’m never going to determine where they are from.

That said, understanding the conditions of this data set, let me get to the actual results. These were found by combining all the follower results from all 450+ different accounts. By combine, only the UserID (A unique number Twitter has assigned for every account) and the country were put in the new file. The duplicate rows were then removed to insure that people who followed multiple accounts were only counted once. This should begin to give an idea as to the comparative distribution globally of Australian sport fans.

Country Count
Australia 77682
India 46174
United States 26515
Unknown 19717
United Kingdom 17547
South Africa 8456
New Zealand 4806
Canada 2004
Ecuador 1819
United Arab Emirates 1325
Ireland 1318
Brazil 1139
Chile 1101
Bangladesh 1085
Pakistan 1062
Argentina 909
Singapore 850
Germany 760
France 712
Japan 670
Indonesia 579
Philippines 535
Spain 430
Netherlands 413
China 310
Sri Lanka 280
Thailand 275
Malaysia 257
Hong Kong 230
Mexico 214
Italy 190
Portugal 159
Qatar 156
Belgium 152
Sweden 140
Kuwait 139
Saudi Arabia 136
Nepal 132
Switzerland 124
Iran 121
Bahrain 119
Norway 119
Venezuela 119
Turkey 108
Colombia 105
Israel 96
Denmark 90
Peru 90
Egypt 73
Costa Rica 72
Romania 67
Russia 62
Greece 61
Puerto Rico 59
Poland 56
Austria 54
Uruguay 48
Finland 44
Jersey 41
Vietnam 38
Serbia 33
Oman 32
South Korea 32
Barbados 30
Cyprus 29
Taiwan 28
Malta 26
Fiji 24
Kenya 24
Mauritius 22
Paraguay 22
Slovenia 22
Panama 21
Vatican City State 21
Nigeria 20
Maldives 19
Morocco 19
Croatia 18
Ghana 18
Guatemala 18
Ukraine 18
Czech Republic 17
Jamaica 17
Jordan 17
Latvia 17
Papua New Guinea 16
Gibraltar 15
Guernsey 14
Guyana 14
Bermuda 13
Botswana 13
Brunei 13
El Salvador 13
Hungary 13
Namibia 12
Dominican Republic 11
Bulgaria 10
Macedonia 10
Mongolia 10
Lebanon 9
Lithuania 9
Luxembourg 9
Honduras 8
Iceland 8
Monaco 8
Tonga 8
Afghanistan 7
Slovakia 7
Tanzania 7
Tunisia 7
Zimbabwe 7
Bolivia 6
Estonia 6
Iraq 6
Kazakhstan 6
Mozambique 6
Nicaragua 6
Trinidad and Tobago 6
Uganda 6
Algeria 5
Cayman Islands 5
New Caledonia 5
Swaziland 5
Antarctica 4
Aruba 4
Bosnia and Herzegovi 4
Cambodia 4
Haiti 4
Isle of Man 4
Samoa 4
Sudan 4
Zambia 4
Andorra 3
Armenia 3
Cameroon 3
French Polynesia 3
Guam 3
Laos 3
London 3
Seychelles 3
Albania 2
Azerbaijan 2
Belarus 2
Bhutan 2
Djibouti 2
Guadeloupe 2
Malawi 2
Marshall Islands 2
Solomon Islands 2
Suriname 2
Angola 1
Ballarat 1
Belize 1
Bendigo 1
Benin 1
Burkina Faso 1
Chad 1
Christmas Island 1
Congo 1
Cook Islands 1
Cuba 1
Ethiopia 1
Faroe Islands 1
Georgia 1
Greenland 1
Grenada 1
Ivory Coast 1
Kyrgyzstan 1
Liberia 1
Libya 1
Liechtenstein 1
Madagascar 1
Moldova 1
Nauru 1
Norfolk Island 1
Northcote 1
Reunion 1
Rwanda 1
Senegal 1
Sierra Leone 1
Somalia 1
Syria 1
Turks and Caicos Isl 1
United Kindom 1
Vanuatu 1
Virgin Islands 1
Western Sahara 1

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Expanded Profile of Australian En-WP users

Posted by Laura on Sunday, 10 October, 2010

My dissertation topic involves doing a demographic and geographic study of Australian sport fandom online. There are several sites and social networks where you can get publicly available demographic data to begin to formulate a picture of the user population, and then segment that population out by interest in a league, sport and athlete. I’ve spent a lot of time looking at Twitter, Facebook and LiveJournal. Recently, partly because of a trip to the Wikimedia Foundation and discussions with a few people at UCNISS, my interest in who was contributing to Australian sport wiki articles on Wikipedia increased.

Finding out who edited Wikipedia articles using publicly available information is a bit of a challenge. The most reliable information for who edited comes from IP address information. IP addresses can provide an idea as to the geographic location of the contributor. It is easy enough, with the help of a friend, to create a tool that pull the history of a Wikipedia article, get a list of IP addresses that edited the article, feed the IP address into another tool that will pull up the general location of the contributor. (One of my favorite visualizations of this type of information is WikipediaVision.) The data isn’t always accurate and if I was looking primarily at New Zealand, a country without its own dedicated IP address range, this would be even less reliable. Still, for my purposes, this data works pretty well.

This data is still pretty limited. There are a lot of articles that are edited by non-anonymous users. Sometimes, it is possible to get demographic and geographic information about Wikipedia contributors by viewing their profile pages. This can just be time consuming to do manually if an article has a large number of contributors as you need to view a lot of user pages. It becomes a deterrence for trying to collect geographic information about article contributors.

I was looking for a more time effective and accurate method of collecting geographic and demographic information about contributors that is publicly available on their user pages. The easiest and quickest way to get this information on a mass scale is to utilize user box information. Many user boxes, when included on a user page, put the user into a category. These categories are often then linked through the Wikipedian category structure. Beyond that, user boxes involve templates. It is easy to get a list of articles (user pages) that the template is included on.

The methodology that I selected from this point is rather straightforward. It involved:

1. Select a category.
2. Copy and paste the list of articles (user pages) in the selected category to an Excel spreadsheet. Sort the list alphabetically. Copy and paste only the user pages to Notepad. Replace * User with blank. Copy and paste this list back to Excel.
3. Create a filter where the cell contains / . Select those cells. Copy them to notepad, replace / with [tab] in order to remove user subpages from the list. Copy this back to Excel. Select only the column with usernames.
4. Run an advance filter in order to remove all duplicate rows.
5. Copy this list back to the dedicated spreadsheet. Label all those users with the category from which they were pulled in a unique column.
6. Repeat steps 1 to 5 until all the categories that you want to have included are included.
7. Merge/Group all the rows by username.

This method may not be the most efficient way of going about doing this. It can probably be improved by automating some of these steps. In my case, step 7 was not able to be completed using Excel. I had to e-mail the file to @woganmay, who I believe converted the file to a mySQL database, used the group feature, converted the results back to csv and e-mailed the file to me.

In my case, I did not complete this for every category. Some categories did not seem worth it time wise as they had too few user pages to be included. In other cases, the categories were just too big to do. This included all the members of User de, User en, User es, User fr, User it, User jp. Only a selected number of categories were included because of time constraints. Data gathering was focused on categories that I perceived would have the greatest number of Australians and other possible contributors to Australian related articles. When these categories were more exhausted, categories with between 1,00 and 5,000 articles were selected.

There are all sort of limitations to this data. First, not everyone includes userboxes on their profile pages. This means that there could be a lot more Australians on Wikipedia than indicated by userbox inclusion on a user page. The assumption for the resulting data is that proportional representation exists for various categories. So while there are X amount of Christians and Y amount of Atheists, the assumption that the relationship between X and Y will always be proportional to the actual population on Wikipedia. Whatever data is available thus has to be viewed as good enough or supplemented by going to individual user ages to see if other information is available when a user appears where no information for someone when running against the history of the article.

Second, even when they do exist, there are often useful pieces of information that are missing. For example, in an Australian context, there is a userbox for Rugby League fans. There is not however a userbox for Australian rules footy fans. There are also not user boxes and categories for fans of NRL or AFL teams. (This type of user box and category exists for National Hockey League teams.)

About halfway through this process, I realized that this data could be useful for analysis beyond who is editing Wikipedia. At the moment, I’ve only totaled data I have for Australians. It is pretty fascinating and would be neat to go further with: How does the proportional size of the Australian Wikipedian population compare against the actual population? Does the size of the Australian Atheist versus Christiah community actively reflect the proportions in Australian society? Or is the Australian Wikipedian community demographically distinct from the greater population?

The following tables include the data based on people who were included in Wikipedians in Australia and its subcategories and Australian Wikipedians. A copy of the raw data can be found at October 9 – Wikipedia English Data – Australians.xls. The data is provided without comment though any attempts at explaining the patterns found are very much appreciated.

Country Count
Bangladesh 3
Canada 2
Egypt 2
India 1
Indonesia 2
Ireland 3
Jamaica 2
Japan 5
New Zealand 17
Papua New Guinea 1
Republic of Ireland 5
Singapore 5
South Africa 2
South Korea 1
Sri Lanka 2
Tanzania 2
Turkey 2
United States 16
State Count
Australian Capital Territory 89
Canterbury 1
New South Wales 345
Northern Territory 5
Otago 1
Queensland 208
South Australia 144
Southland 1
Tasmania 54
Victoria 370
Wellington 2
Western Australia 145
Degree Count
BA degrees 21
BCom degrees 2
BCS degrees 3
BE degrees 18
BMus degrees 1
BS degrees 41
MS degrees 5
PhD degrees 18
University/Alma Mater Count
Australian National University 14
Avondale College 1
Charles Sturt University 1
Curtin University of Technology 7
Deakin University 6
Flinders University 7
Griffith University 1
James Cook University 2
La Trobe University 2
Macquarie University 5
Massey University 1
Monash University 19
Royal Melbourne Institute of Technology 10
University of Adelaide 4
University of Alberta 1
University of Canberra 3
University of Melbourne 21
University of New England 4
University of New South Wales 24
University of Newcastle 8
University of Sydney 16
University of Tasmania 3
University of Technology, Sydney 4
University of Western Australia 11
University of Wollongong 4
Victorian College of the Arts 1
Student type Count
Business students 3
College students 26
Law students 9
Medical students 8
University students 59
Website Count
Open Directory Project 1
OpenStreetMap 2
Wookieepedia 1
Religion Count
Anglican and Episcopalian 8
Antitheist 3
Atheist 97
Buddhist 13
Catholic 7
Christian 47
Eastern Orthodox 2
Hindu 1
Jewish 4
Lutheran 1
Methodist 2
Muslim 4
Non-denominational Christian 2
Objectivist 2
Pastafarian 17
Presbyterian 3
Protestant 11
Roman Catholic 10
Ethnicity and nationality Count
Argentine 2
Bangladeshi 2
British 3
English 10
Latino/Hispanic 1
Skill Count
Aircraft pilots 5
Artists 3
Engineers 17
Filmmakers 17
Homebrewers 10
Mechanical engineers 1
Professional writers 1
Surfers 2
Profession Count
Accountants 2
Actor 5
Actuaries 2
Aircraft pilots 5
Biologist 9
Broadcasters 5
Chemist 6
Composers 28
Computer scientists 7
Engineers 17
Filmmakers 17
Geoscientists 2
Mechanical engineers 1
Scientists 7
Teacher 18
University teacher 4
Web designers 2
Web developers 1
Interest Count
Chemistry 27
Cooking 1
Physics 34
Strings (physics) 6
Sports Count
Cavers 2
Cross-country runners 4
Dancers 3
Detroit Red Wings fans 2
Equestrians 2
Fencers 2
Geocachers 8
Hikers 2
Hunters 7
Outdoor pursuits 2
Rugby league fans 50
Runners 2
Sailing 1
Scuba divers 8
Snowboarders 2
Swimmers 16
Swing dancers 1
Toronto Maple Leafs fans 1
Ultimate Fighting Championship fans 2
Vancouver Canucks fans 3
WikiProject Tennis members 4
Wikipedia Status Count
Administrator hopefuls 41
Administrators 45
Administrators who will provide copies of deleted articles 11
Bureaucrats 1
Contribute to Wikimedia Commons 1
Create userboxes 3
Opted out of automatic signing 4
Reviewers 10
Rollbackers 27
Service Award Level 01 12
Service Award Level 02 14
Service Award Level 03 10
Service Award Level 04 5
Service Award Level 05 6
Service Award Level 06 9
Service Award Level 07 11
Service Award Level 08 3
Service Award Level 09 2
Wikimedia Commons administrators 2
Philosophy Count
Hindu 1
Humanist 6
Materialist 9
Pastafarian 16
Theist 9

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Methodology: Draft/Free writing (part 5)

Posted by Laura on Tuesday, 5 October, 2010

This feels mostly finished in terms of content to add to it. I need to get feedback from my supervisors regarding what is missing or what could be improved. (I know it isn’t perfect. I just wanted it written so I could get on with editing it and realizing where the problems are.) Any and all feedback is appreciated.



Methodology


Types of Social Media Research

When conducting social media research, there are ten general methods that can be used to gather and analyze data. These are:

  1. Individual case studies for how a business uses social media and the web;
  2. Search and traffic analytics analysis;
  3. Sentiment analysis and reputation management;
  4. Content analysis;
  5. Usability studies;
  6. Interaction and collaboration analysis;
  7. Relationship analysis to try to determine how people interact and to identify key influencers;
  8. Population/demographic studies;
  9. Online target analysis of behavior and psychographics; and
  10. Predictive analysis.

Each of these methods offers insights into various aspects of the web and its population. The type of analysis used is often specific to the purpose of the research, involved blended approaches from traditional analysis types, and different methods are often used in conjunction with each other. These methods often blend quantitative and qualitative analysis. Choosing the correct method of gathering analyzing data can be one of the biggest hurdles for being able to measure ROI and understand how a community works.

This section will provide a brief summary of each type, explain how to conduct this type of research and give examples that used that methodology.

Individual case studies for how a business uses social media and the web.

Case studies on social media usage are often done to measure the effectiveness of specific actions taken by an organization.

Bronwyn et al. (2005) say case studies “typically examine the interplay of all variables in order to provide as complete an understanding of an event or situation as possible. This type of comprehensive understanding is arrived at through a process known as thick description, which involves an in-depth description of the entity being evaluated, the circumstances under which it is used, the characteristics of the people involved in it, and the nature of the community in which it is located.”

This methodology often incorporates components of all the other methods discussed in this section. The specific methods often depend on the goals of the person or organization conducting the case study.

Vincenzini (2010) did a case study regarding the use of the social media by the NBA in an attempt to define why they have been successful in using it to promote the league. The author used quantitative analysis to measure the size of the community, the volume of content they were viewing on sites like YouTube and the volume of content they were creating on sites like Twitter. The quantitative analysis was synthesized with explanations from NBA employees to explain their practices in the context of their own business decisions as they pertained to social media. This was followed up with an explanation as to what worked and what did not worked and offered advise for others involved with sport and social media to help them leverage their own position.

Case studies are a mixed methodology approach, borrowing from other approaches. The major difference is that the case study focuses on a narrower perspective with the goal of tracking behavioral changes, or in advising others on how an organization changed practices and how those lessons can be applied elsewhere.

Search and traffic analytics analysis.

Search engine and traffic analytics generally is done internally to determine how to optimize a site in order to increase the amount of visitors a site gets and the total number of pages that they view. This method involves identifying how people arrive at a specific site and the pages they visit while at the site. Traffic analytics analysis often includes six different components: Search engine visitors, paid search advertisements, pay per click, organic traffic, direct traffic and internal site traffic.

Ramos and Cota (2009) define traffic analytics as “Tools that analyze and compare customer activity in order to make business decisions and increase sales. Analytics tools can report the number of conversions, the keywords that brought conversions, the sites that sent converting traffic, conversion by campaign, and so on.”

There are a number of different methods and tools that allow for this type of analysis. Early in the history of the Internet, one of the most popular tools and methods involved analyzing web server log files. (Jansen 2009) Another popular early method of analysis was page tagging, which involved embedding an invisible image on a page, which, when the image is triggered, “triggers JavaScript to send information about the page and the user back to a remote server.” (Jansen 2009) These earlier tools have advanced a bit and now include tools like Quantcast and Google Analytics. Kaushik (2010) recommends Google Analytics, a free tool that involves putting a bit of code on all pages of a site. Kaushik (2010) points out that various types of traffic analysis can be done using the various tools provided by Google Analytics. The author claims that Google Analytics allows you to break the analysis down into “three important pieces: campaign response, website behavior, and business outcomes.” (Kaushik 2010)

Fang (2007) completed a case study at the Rutgers-Newark Law Library in order to track library website usage, track visitor behavior and determine how to improve the website to better serve users. Earlier work done by the library had involved surveys handed out to patrons, analysis of log files, and the use of counters. (Fang 2007) The author changed methods because of some inherent flaws in using those methods to analyze website needs. They used Google Analytics in order to track user activity on the library’s website. The library “found out how many users were accurately following the path we had designed to reach a target page.” (Fang 2007) This sort of path following navigation was one of the goals they had when they designed their site. They also found out that “Visitor Segmentation showed that 83% of visitors were coming from the United States. About 50% of U.S. visitors were from New Jersey, and 76% of these were from Belleville and Newark. These results matched our predictions for patrons’ geographical patterns.” (Fang 2007) The results of this analysis enabled the library to make changes to improve their website.

This type of methodology lends itself more to a case study approach and often requires the consent of the website involved in order to get private logs. It can be used in conjunction with other methods, but should be used in a more targeted analysis of highly specific research areas.

Sentiment analysis and reputation management.

Sentiment analysis involves identifying content related to a topic and identifying the emotion connected to that content. In a sport context, sentiment analysis could involve determining if newspapers are providing positive or negative coverage of a team. In a social media context, sentiment analysis would involve determining attitudes being expressed on Twitter in individual tweets. Reputation management goes one further: After sentiment has been determined, a decision needs to be made on if and how negative and positive sentiment content should be responded to. Sentiment analysis is passive analysis where non-stakeholders can conduct analysis. Reputation management is active analysis that is primarily conducted by stakeholders as part of on going activities to improve a brand, be it personal or corporate.

While sentiment analysis and reputation management are similar in their desire to monitor a response to a situation, the tools available vary differently for each type. There a variety of different tools for sentiment analysis. One of the tools for conducting sentiment analysis are freely available lists of words “that evoke positive or negative associations.” (Wanner et al. 2009) Sterne (2010) suggests that content being ReTweeted on Twitter can be seen as a tool to measure positive sentiment. Sterne (2010) suggests that the ratio of follows/followers is not an effective tool for measuring sentiment on Twitter. Reputation management tools include Trackur. It allows you to “set up searchers and the system automatically monitors the Web for key words that appear on news sites, blogs, and other social media.” (Weber 2009)

Wanner et al. (2009) did a sentiment analysis of RSS feeds that focused on the 2008 United States presidential elections. They selected 50 feeds connected to the elections and collected updates to these feeds every 30 minutes for one-month starting 9 October 2008. For each item they collected off the feeds, they also recorded the date, title, description and feed id. (Wanner et al. 2009) After that, they eliminated all noise, which mostly consisted of non-content like URLS. (Wanner et al. 2009) The next step was to filter out all items that did not contain one of the following terms: “Obama”, “McCain”, “Biden”, “Palin”, “Democrat” and “Republican”. (Wanner et al. 2009) Sentiment was then analyzed using freely available lists “that evoke positive or negative associations.” (Wanner et al. 2009) The results were then visualized. Five events that happened during this period were chosen for a more detailed visual examination. They found that the news regarding possible abuse of power by Sarah Palin in Alaska resulted in many negative posts. They also found that the debates resulted in low sentiment scores for both candidates as the candidates attacked each other. The authors concluded that the visual tool they created would be useful for monitoring public debates.

This methodology can overlap with influencer identification (Weber 2009) as part of reputation management involves determining which people are worth responding to. It can also overlap with psychographics. Despite the obvious overlaps, this type of research often appears independently and not as part of a larger study.

Content analysis.

Content analysis involves looking at the individual components of something larger and analyzing it. In a social media context, the content could be comments on a Facebook fanpage, or all the tweets made by a person or group. Content analysis can be either qualitative or quantitative, depending on the purpose of the research.

With content analysis, the researcher views data as “data as representation not of physical events but of texts, images and expressions that are created to be seen, read, interpreted, and acted on for their meanings, and therefore be analyzed with such uses in mind.” (Krippendorff 2007) Krippendorff (2007) defines the basic methodology used in content analysis as unitizing, sampling, recording, reducing, inferring, and narrating.

An example of content analysis is a 2009 study by Kian, Mondello, & Vincent. It looked at ESPN and CBS’s internet coverage of men and women’s NCAA basketball tournament, also called March Madness. The methodology was spelled out by the authors as: “All 249 (N D 249) byline articles from CBS SportsLine and ESPN Internet were read, coded, and content analyzed to determine the descriptors in Internet articles.” (Kian, E., Mondello, M., & Vincent, J., 2009) The authors used multiple coders to help prevent bias in terms of interpretation of gendered language. The two sites in the sample were chosen because they were the largest. All types of March Madness content was included. Only the text of the content was included. Titles and authors were not. Categories for encoding gendered language were based on previous research by sport media researchers. Only descriptors were used for encoding. Totals for gendered descriptors were then calculated and an analysis was completed.

This method of analysis is can be done with other forms of analysis like sentiment analysis, as part of a usability study or collaboration study. It can also be done separately. It often appears most successful when done separately as part of a larger study to help provide context for other data analysis.

Usability studies.

In a social media context, usability studies look at how people use some aspect of the Internet or software that connects to it.

According to Sweeney, Dorey and MacLellan (2006), one of the purposes of a usability study is “point out specific usability problems with your Web site interface in line with how well your Web site speaks to your audience and their goals.” Jerz (2002) cautions that “Simply gathering opinions is not usability testing — you must arrange an experiment that measures a subject’s ability to use your document.” That caution also explains the general methodology of a usability study outlined by Jerz (2002): Collect both quantitative and qualitative data. The quantitative should involve some type of measurement. The qualitative should allow testers to express their opinions. Jerz (2002) suggests that you use at least five tests for the first run. Then, after fixing errors and problems based on tester feedback, you get another five testers to test the site to determine that those errors have been fixed.

An example of a usability study is one conducted by Sturgil, A., Pierce, R., & Wang, Y. (2010). The study tried to determine how much content readers of Internet news sites really wanted. The methodology involved conducting a focus group, and think-aloud sessions. In both cases, the researchers observed participants using Internet news sites. They also asked them questions regarding what content they visited and why. The methodology relied heavily on qualitative analysis with a small quantitative component.

Usability studies can be done in conjunction with traffic analysis and search analytics as the purposes are often similar: Improve the user experience and try to get users to complete certain tasks.

Interaction and collaboration analysis.

Interaction and collaboration analysis focuses on how people work together in an online environment. Collaboration analysis often looks more at how people work together to create something, such as contributing to a wiki or to create an event like an unconference, where everyone is working towards a common goal. Interaction analysis tends to focus on how people engage each other when there is no common goal in the group.

Software Services, Dale Carnegie & Associates, Inc., & Shedletsky, L. (2000) explain the methodology for interaction analysis. They encourage researchers to look at topics discussed, purposes of individuals’ utterances, structure of conversation, and how properties of talk affect outcomes when completing an interaction analysis. The researcher should determine the setting for which this type of analysis will be conducted: In a controlled setting such as a laboratory, by selecting samples of existing conversations, or by examining all conversation that the research is capable of overhearing. The researcher needs to determine if they will use prompted or unprompted interaction. They also need to determine how they will record conversations and if their analysis will be quantitative or qualitative in nature. Once these things have been determined, then a methodology for data collection can be figured out.

Viégas et al. (2007) did a collaboration analysis focusing on Wikipedia. The purpose of their work was to examine historical editing patterns and how editing practices have evolved over time. They built on work done by Viégas, Wattenberg and Dave in 2003. The methodology they used involved getting the editing history of articles across several different Wikipedia namespaces. The history of the articles was then examined using several visualization tools, metrics and methods depending on the established cultural practices for that namespace. One tool they used was a history flow visualization application. A method they used was the manual classification of “all user posts in a purposeful sample”. (Viégas et al., 2007) Metrics they used included count of horizontal rules, signed user names, new indentations levels, votes in polls and total “references to internal Wikipedia resources.” (Viégas et al., 2007) These tools, metrics and methods allowed them to examine how collaboration and interaction had changed over time.

This type of analysis often stands alone. It could be used as part of a usability study or relationship analysis to provide context for the results of those analysis types.

Relationship analysis.

Relationship analysis involves examining the relationships between users on a social network, message board or mailing list. The goal is to identify cliques of different sizes or people who are particularly influential in a particular group online. This type of analysis is important to many brands including Starbucks (Plimsoll, 2010). The purpose of relationship analysis is to identify key influencers and social who influencers who are or who have the potential to be brand evangelists. (Plimsoll, 2010)

Lord and Singh (2010) define social media influence marketing as being “about recognizing, accounting and tapping into the fact that as your potential consumer makes a purchasing decision, he or she is being influenced by different circles of people through conversations with them, both online and off.”

The methodology for influence identification is not clearly spelled out as identifying influencers can be heavily dependent on the network being examined and how the community on a specific site functions. As a result, social media marketers suggest an array of tools like Twitalyzer that can be used to help determine your own influence. (Ankeny 2009) Twitalyzer’s Peterson and Katz (2010) explain their site-specific method of determining influence as including the following variables: Engagement level, total followers, total following, hashtags cited, lists included on, frequency of updates, references by others, references of others, times content is retweeted, urls cited and a number of other variables. Sterne (2010) suggests using WeFollow.com to find people who use topic specific #hashtags on Twitter. The people who tweet the most about a topic are likely to be influencers in that others looking for tweets around a topic are likely to read them. In a wider web context, Sterne (2010) suggests using Technorati to identify bloggers who have clout and influence around a certain topic.

This type of research can be viewed as a fundamental component to sentiment analysis; social media marketing companies like Razorfish often package the two together. (Lord & Singh, 2010)

Population /Demographic studies.

Population studies involve defining the demographic characteristics of a community. In a population study, the goal is also to define the limits and size of the community that is being studied. Because of the complexity in defining the boundaries of a population and in sampling the whole of it, this type of research is rarely done in terms of social media.

Daugherty and Kammeyer (1995) define a population study as the assembling “of numerical data on the sizes of populations.” This sort of data is defined by the authors as “descriptive demographic statistics.” Daugherty and Kammeyer (1995) say “population numbers are always changing, so even if they are accurate when gathered they are soon out of date and inaccurate.” Daugherty and Kammeyer (1995) say the basic purpose of conducting a demographic study “is to explain or predict changes or variations in the population variables or characteristics.” Given the definition of a population study, the methodology involves counting all members of a select population.

The most famous example of a population study is the census. In the United States, this is done every ten years. According to the U.S. Census Bureau (n.d), the goal of the 2010 US census is ” to count all U.S. residents—citizens and non-citizens alike.” This is done by sending all citizens a ten-question questionnaire, requiring that people complete it by law and having a census taker follow up for all households did not return completed questionnaires. (U.S. Census Bureau, n.d.) The results are then calculated and are used by the government to make decisions.

This type of research often stands on its own. The results will often be utilized for marketing purposes in conducting other research, such as psychographics, to make that that sampling contains representative populations.

Online target analysis of behavior and psychographics.

Online targeting of and marketing towards a specific audience because of their demographic characteristics is extremely common on the Internet. Psychographics is a term that includes targeting towards a specific demographic group except it includes the offline component.

Sutherland and Canwell (2004) define psychographics as “market research and market segmentation technique used to measure lifestyles and to develop lifestyle classifications.” (p. 247) Nicolas (2009) defines online behaviorial analysis as a series of steps: Collecting user data across several sites, organizing information about users based on the sites they visit and their behavior on those sites, “infer demographics and interest data”, and classifying new users based on the collected data in order to deliver relevant ads and content based their demographic profiles. Kinney, McDaniel, and DeGaris (2008) define psychographics as attitude towards something such as a brand or involvement with an organization.

Given the methodology involved, much of this type of research involves action research in that it is done in a specific content, based on internal models to address specific situations.

An example of this type of research was done by Kinney, McDaniel, and DeGaris (2008) who investigated the demographic characteristics of NASCAR fans and their attitudes towards NASCAR, its sponsors and sponsor involvement with NASCAR. The research found that age, gender and education were all important variables in determining sponsor recall: Younger, more educated males had the best brand recall amongst NASCAR fans.

This type of research can be viewed as a subcomponent of a population study in that demographic information is sought about the population. In an online context, it often works in conjunction with search and traffic analytics analysis, content analysis, and interaction and collaboration analysis.

Predictive analysis.

A search on 13 July 2010 on SPORTDiscus had three results for “predictive analysis.” A search on the same date on Scopus had 605 results, 275 of which were in engineering, 132 in computer science and 102 in medicine. Predictive analysis is probably one of the least used analysis methods, especially in social media and fandom.

What is predictive analysis? At its simplest, it is identifying a future event or events, monitoring selection actions that precede the event and seeing if those events can be used to predict the outcome of similar events in the future. If a predictive value is found, an organization can monitor behaviors to help make more informed decisions.

An example of this type of research is “Predicting the Future With Social Media” by Asur and Huberman (2010). Their goal was to determine if tweet volume and sentiment on Twitter prior to a movie being released could be used to predict how well a movie performs at the box office. Their methodology involved identifying movie wider release dates that took place on a Friday, creating a list of keyword searches related to those movies, and using the Twitter API to collect all tweets and aggregate date that mention those keywords over a three month time period. The authors then compared the tweet volume to box office performance. They concluded that social media “can be used to build a powerful model for predicting movie box-office revenue.” (Asur & Huberman, 2010)

This type of research can be used in conjunction with other methods. It can be used along side a population study to see if certain actions will result in demographic changes.

Rational for Population Study

The literature review provides insight into the lack general quantitative analysis regarding the demographic and geographic characteristics of Australian sport fans in general and AFL fans in specific. Much of what is written involves observations based on match attendance, attendance statistics, common historical tropes based on the experience of the authors as members of the sport community or analysis based on demographic data around the community for which a club was based. The methodology rarely is spelled out. There is little reason to doubt the demographic composition of fans because most accounts match very well and there are a variety of citations that refer to a wide variety of sources. The literature review demonstrates a lack of quantitative research in terms of population characteristics.

The research regarding fan demographics in the Australian sport online community is even sparser. The focus on research being done tends to focus on fan production, such as the transition from fanzines to online mailing lists. It is often not quantitative in nature.

Given the hole in the research, there is a clear need to fill it to better understand the existing population of AFL fans who are increasingly using the Internet in order to facilitate their love of their chosen club. To do this, an appropriate methodology needs to be chosen. The previous section examined the major methodological approaches available for conducting research into social media and online populations. Most of these methods involve some form of interaction analysis or textual analysis. They do not offer a clear method of understanding the characteristics of a large group and its subcomponents.

Methodological Approach

The methodology used in this study will be a population study. To provide context for the findings, other methods will be utilized. The exact method for conducting the population study will differ depending on the site being examined. Therefore, most of the methodology used in this study will appear inside specific chapters.

Throughout this study, there is a dependence on user listed locations to determine the geographic location of members of the Australian sport fan community. To provide consistency across all sites looked at, a list was developed that included user generated location, city, state and country. The city, state and country were determined based on intelligent guesswork. For example, as the focus of the research is Australia, if Melbourne was standing alone, the assumption was that the user meant Melbourne, Victoria, Australia and not Melbourne, Florida, United States. Spelling variations and nicknames were also used to determine location. For example, Brisvegas is a nickname for Brisbane, Queensland, Australia. Often patterns of cities were looked for assuming the standard pattern of city, state, country or country, state, city or city, country or city, state. To aid in processing location lists more quickly, when using an automated tool such as the one for Twitter, the user-generated list was supplemented with one created by the author. This list included all Australian cities listed using the patterns of postal code and city, state and city, country, and city, state, country. The completed list contains over 65,000 variants that were listed by the author or user created.

References

Ankeny, J. (2009). HOW TWITTER IS REVOLUTIONIZING BUSINESS. Entrepreneur, 37(12), 26-32. Retrieved from Business Source Premier database.

Asur, S., & Huberman, B. A. (2010). Predicting the Future With Social Media. Social Computing Lab. Retrieved from http://www.hpl.hp.com/research/scl/papers/socialmedia/socialmedia.pdf

Bronwyn, B., Dawson, P., Devine, K., Hannum, C., Hill, S., Leydens, J., Matuskevich, D. Traver, C, and Palmquist, M. (2005). Case Studies. Writing@CSU. Colorado State University Department of English. Retrieved August 26, 2010 from http://writing.colostate.edu/guides/research/casestudy/.

Daugherty, H. G., & Kammeyer, K. C. W. (1995). An introduction to population. New York: Guilford Press.

Fang, W. (2007), Using Google Analytics for improving library website content and design: A case study. Library Philosophy and Practice 2007 (June), LPP Special Issue on Libraries and Google. Retrieved September 2, 2010 from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.84.5924&rep=rep1&type=pdf

Jansen, B. J. (2009). Understanding user-Web interactions via Web analytics. San Rafael, Calif.: Morgan & Claypool Publishers.

Jerz, D. (2002, November 11). Usability Testing: What is it? Dennis G. Jerz; Seton Hill University. Retrieved October 4, 2010, from http://jerz.setonhill.edu/design/usability/intro.htm

Kaushik, A. (2010). Web analytics 2.0: The art of online accountability & science of customer centricity. Hoboken, N.J: Wiley.

Kian, E., Mondello, M., & Vincent, J. (2009). ESPN—The Women’s Sports Network? A Content Analysis of Internet Coverage of March Madness. Journal of Broadcasting & Electronic Media, 53(3), 477-495. doi:10.1080/08838150903102519.

Kinney, L., McDaniel, S., & DeGaris, L. (2008). Demographic and psychographic variables predicting NASCAR sponsor brand recall. International Journal of Sports Marketing & Sponsorship, 9(3), 169-179. Retrieved from SPORTDiscus with Full Text database.

Krippendorff, K. (2007). Content analysis: An introduction to its methodology. Thousand Oaks, Calif.

Lord, B., & Singh, S. (2010). Fluent: The Razorfish Social Influence Marketing Report. Razorfish. Retrieved August 25, 2010, from http://fluent.razorfish.com/publication/?m=6540&l=1

Nicolas, P. (2009, December 17). “Online audience behavior analysis and targeting.” Patrick Nicolas Official Home Page. Retrieved August 1, 2010, from http://www.pnexpert.com/Analytics.html

Peterson, E., & Katz, J. (2010). Twitalyzer Help and Company Information | Twitalyzer: Serious Analytics for Social Media and Social CRM. Twitalyzer. Retrieved August 25, 2010, from http://www.twitalyzer.com/help.asp

Plimsoll, S. (2010). Find and target customers in the social media maze. Marketing (00253650), 10-11. Retrieved from Business Source Premier database.

Ramos, A., & Cota, S. (2009). Search engine marketing. New York: McGraw-Hill.

Software Services, Dale Carnegie & Associates, Inc., & Shedletsky, L. (2000, September 7). Interaction Analysis. University of Southern Main Communications Department. Retrieved October 5, 2010, from http://www.usm.maine.edu/com/chapter8/

Sterne, J. (2010). Social media metrics: How to measure and optimize your marketing investment. Hoboken, N.J: John Wiley.

Sturgil, A., Pierce, R., & Wang, Y. (2010). Online News Websites: How Much Content Do Young Adults Want?. Journal of Magazine & New Media Research, 11(2), 1-18. Retrieved from Communication & Mass Media Complete database.

Sutherland, J., & Canwell, D. (204). Key Concepts in Marketing. Palgrave Key Concepts. Hampshire, England: Palgrave MacMillan.

Sweeney, S., Dorey, E., & MacLellan, A. (2006). 3G marketing on the internet: Third generation internet marketing strategies for online success. Gulf Breeze, FL: Maximum Press.

Viégas, F. B., Wattenberg, M., Kriss, J., & van Ham, F. (2007). Talk Before You Type: Coordination in Wikipedia. Proceedings of the 40th Hawaii International Conference on System Sciences. Big Island, Hawaii. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.84.6907&rep=rep1&type=pdf

Vincenzini, A. (2010, July 14). A Case Study: The NBA’s Social Media Strategy & Tactics. Retrieved August 26, 2010, from http://www.slideshare.net/AdamVincenzini/the-nba-and-social-media-a-case-study

U.S. Census Bureau. (n.d.). How We Count America – 2010 Census. U.S. Census Bureau. Retrieved August 28, 2010, from http://2010.census.gov/2010census/how/how-we-count.php

Wanner, F., Rohrdantz, C., Mansmann, F., Oelke, D. and Keim, D., Visual Sentiment Analysis of RSS News Feeds Featuring the US Presidential Election in 2008. in Workshop on Visual Interfaces to the Social and Semantic Web, (2009).

Weber, L. (2009). Sticks and Stones: How Digital Business Reputations Are Created over Time and Lost in a Click. John Wiley & Sons Inc.

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Who is more sport mad (re: professional sport)? Americans or Aussies? Methodology question

Posted by Laura on Saturday, 11 September, 2010

I had a discussion the other day. My observations, based in a small part based on my American issues, is that Americans are more sport bad and are more likely to support their professional sport teams than Australians are. These sort of allegiances (well expressing them) just appear more fundamental to being an American than being an Australian. (This is based on the perception of people wearing sport related apparel for local teams.) The question is: How do you test this? Is there a way to prove that Americans or Aussies are more likely to barrack for their national professional leagues and clubs?

I’m not sure how feasible doing that is and any methodology seems like it could have holes easily torn in it. That said, coming from a place of some one who likes online population studies, the following is my proposed methodology:

  1. Get all the follower location data for people who follow the major national based leagues Twitter accounts in the United States and Australia.  In this case, @AFL, @NRL for Australia and @MLB, @NCAA, @NFL, @NBA for the USA.
  2. Get a follower count of the total Australians following Australia’s leagues.  Get a count of Americans following Australian leagues.  Get a follower count of the total Americans following American leagues.  Get a follower count of the total Australians following American leagues.
  3. Determine the percentage of the total population in Australia following Australian and American leagues.  Determine the percentage of the total population of the USA following Australian and American leagues.
  4. Repeat the above process focusing on local teams: @sydneyswans, @nrl_bulldogs for Sydney. @stkildafc @MelbStormRLC for Melbourne. @NHLBlackhawks , @chicagobulls @whitesox @Cubs for Chicago. @Mets , @YANKEES, @thenyknicks , @nyjets for New York.  Instead of national focus, include only population from the metro area for each city.

Combine the above with a Facebook geography:

  1. Go to https://www.facebook.com/ads/create/ . Get the total population of fans in Australia and the United States for the AFL, A-League, W-League, NRL, WNBL, and ANZ Championship.  Get the total population of fans in Australia and the United States for the NFL, NBA, MLB, NHL, NCAA, MLS, WPS and WNBA.
  2. Determine what the percentage of Facebook fans is relative to the total population of the country.
  3. Repeat for local teams in Sydney, Melbourne and Brisbane in Australia, Chicago, New York, Los Angeles in the United States.  Count only people living with in 50 miles of the city.

The results should show that one country has a greater population expressing interest in professional sport.  This country could them be deemed more sport mad than the other.

Does it sound like a methodology that could show what I think it shows?  Are there any better methods for determining which country is more sport mad?


So I decided to get some of this data for Facebook. When getting interests, I tried to use every relevant interest related to a team. The population data for the metro regions comes from Wikipedia. The population data for the country comes from Google. Analysis later. Early observation: Australia as a whole more dedicated to leagues. Americans are more dedicated to local club support.

League/Team Based in Country/City Fans Country/Metro population % fans Country/Metro population on Facebook % fans
AFL Australia Australia 261,580 21,431,800 1.221% 9,621,400 2.719%
AFL Australia United States 4,720 307,006,550 0.002% 133,925,380 0.004%
NBA United States Australia 5,040 21,431,800 0.024% 9,621,400 0.052%
NBA United States United States 573,340 307,006,550 0.187% 133,925,380 0.428%
NFL United States Australia 3,860 21,431,800 0.018% 9,621,400 0.040%
NFL United States United States 438,020 307,006,550 0.143% 133,925,380 0.327%
NRL Australia Australia 71,100 21,431,800 0.332% 9,621,400 0.739%
NRL Australia United States 940 307,006,550 0.000% 133,925,380 0.001%
WNBA United States Australia 20 21,431,800 0.000% 9,621,400 0.000%
WNBA United States United States 3,740 307,006,550 0.001% 133,925,380 0.003%
A-League Australia Australia 3,900 21,431,800 0.018% 9,621,400 0.041%
A-League Australia United States 100 307,006,550 0.000% 133,925,380 0.000%
NHL United States Australia 4,920 21,431,800 0.023% 9,621,400 0.051%
NHL United States United States 447,000 307,006,550 0.146% 133,925,380 0.334%
NCAA United States Australia 320 21,431,800 0.001% 9,621,400 0.003%
NCAA United States United States 254,560 307,006,550 0.083% 133,925,380 0.190%
ANZ Championship Australia Australia 1,480 21,431,800 0.007% 9,621,400 0.015%
ANZ Championship Australia United States 20 307,006,550 0.000% 133,925,380 0.000%
WPS United States Australia 18,100 21,431,800 0.084% 9,621,400 0.188%
WPS United States United States 160 307,006,550 0.000% 133,925,380 0.000%
W-League Australia Australia 1,340 21,431,800 0.006% 9,621,400 0.014%
W-League Australia United States 60 307,006,550 0.000% 133,925,380 0.000%
Chicago Cubs Chicago Chicago 819,260 9,569,624 8.561% 5,915,800 13.849%
Chicago Bears Chicago Chicago 825,400 9,569,624 8.625% 5,915,800 13.952%
Chicago Bulls Chicago Chicago 373,640 9,569,624 3.904% 5,915,800 6.316%
Chicago Blackhawls Chicago Chicago 360,940 9,569,624 3.772% 5,915,800 6.101%
Chicago White Sox Chicago Chicago 211,160 9,569,624 2.207% 5,915,800 3.569%
Chicago Fire Chicago Chicago 23,900 9,569,624 0.250% 5,915,800 0.404%
Chicago Bandits Chicago Chicago 1,140 9,569,624 0.012% 5,915,800 0.019%
Brisbane Broncos Brisbane Brisbane 41,160 2,004,262 2.054% 1,422,400 2.894%
Brisbane Lions Brisbane Brisbane 6,680 2,004,262 0.333% 1,422,400 0.470%
Queensland Maroons Brisbane Brisbane 68,900 2,004,262 3.438% 1,422,400 4.844%
Queensland Reds Brisbane Brisbane 4,880 2,004,262 0.243% 1,422,400 0.343%
Queensland Roar Brisbane Brisbane 2,480 2,004,262 0.124% 1,422,400 0.174%

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Methodology: Draft/Free writing (part 4)

Posted by Laura on Thursday, 2 September, 2010

When I say part 4, what I mean is that this this includes everything you’ve seen in earlier parts… and I’ve just added bits here and there. In this case, I’ve added about 800 words. I’ve managed to complete about seven of the ten sections of my methodology so far. Even though seven sections are done, this is still very much a draft as the grammar has issues, there are organizational issues, there may be incomplete thoughts, etc. A lot of this blog is about showing other people the process of writing a dissertation. As I’m writing about social media and social media moves so fast… posting this in this format feels appropriate. Onwards with my latest…


Methodology

When conducting social media research, there are ten general methods that can be used to gather and analyze data. These are:

  1. Individual case studies for how a business uses social media and the web;
  2. Search and traffic analytics analysis;
  3. Sentiment analysis and reputation management;
  4. Content analysis;
  5. Usability studies;
  6. Interaction and collaboration analysis;
  7. Relationship analysis to try to determine how people interact and to identify key influencers;
  8. Population/demographic studies;
  9. Online target analysis of behavior and psychographics; and
  10. Predictive analysis.

Each of these methods offers insights into various aspects of the web and its population. The type of analysis used is often specific to the purpose of the research, involved blended approaches from traditional analysis types, and different methods are often used in conjunction with each other. These methods often blend quantitative and qualitative analysis. Choosing the correct method of gathering analyzing data can be one of the biggest hurdles for being able to measure ROI and understand how a community works.

This section will provide a brief summary of each type, explain how to conduct this type of research and give examples that used that methodology.

Individual case studies for how a business uses social media and the web (COMPLETE)

Case studies on social media usage are often done to measure the effectiveness of specific actions taken by an organization.

Bronwyn et al. (2005) say case studies “typically examine the interplay of all variables in order to provide as complete an understanding of an event or situation as possible. This type of comprehensive understanding is arrived at through a process known as thick description, which involves an in-depth description of the entity being evaluated, the circumstances under which it is used, the characteristics of the people involved in it, and the nature of the community in which it is located.”

This methodology often incorporates components of all the other methods discussed in this section. The specific methods often depend on the goals of the person or organization conducting the case study.

Vincenzini (2010) did a case study regarding the use of the social media by the NBA in an attempt to define why they have been successful in using it to promote the league. The author used quantitative analysis to measure the size of the community, the volume of content they were viewing on sites like YouTube and the volume of content they were creating on sites like Twitter. The quantitative analysis was synthesized with explanations from NBA employees to explain their practices in the context of their own business decisions as they pertained to social media. This was followed up with an explanation as to what worked and what did not worked and offered advise for others involved with sport and social media to help them leverage their own position.

Case studies are a mixed methodology approach, borrowing from other approaches. The major difference is that the case study focuses on a narrower perspective with the goal of tracking behavioral changes, or in advising others on how an organization changed practices and how those lessons can be applied elsewhere.

Search and traffic analytics analysis (COMPLETE)

Search engine and traffic analytics generally is done internally to determine how to optimize a site in order to increase the amount of visitors a site gets and the total number of pages that they view. This method involves identifying how people arrive at a specific site and the pages they visit while at the site. Traffic analytics analysis often includes six different components: Search engine visitors, paid search advertisements, pay per click, organic traffic, direct traffic and internal site traffic.

Ramos and Cota (2009) define traffic analytics as “Tools that analyze and compare customer activity in order to make business decisions and increase sales. Analytics tools can report the number of conversions, the keywords that brought conversions, the sites that sent converting traffic, conversion by campaign, and so on.”

There are a number of different methods and tools that allow for this type of analysis. Early in the history of the Internet, one of the most popular tools and methods involved analyzing web server log files. (Jansen 2009) Another popular early method of analysis was page tagging, which involved embedding an invisible image on a page, which, when the image is triggered, “triggers JavaScript to send information about the page and the user back to a remote server.” (Jansen 2009) These earlier tools have advanced a bit and now include tools like Quantcast and Google Analytics. Kaushik (2010) recommends Google Analytics, a free tool that involves putting a bit of code on all pages of a site. Kaushik (2010) points out that various types of traffic analysis can be done using the various tools provided by Google Analytics. The author claims that Google Analytics allows you to break the analysis down into “three important pieces: campaign response, website behavior, and business outcomes.” (Kaushik 2010)

Fang (2007) completed a case study at the Rutgers-Newark Law Library in order to track library website usage, track visitor behavior and determine how to improve the website to better serve users. Earlier work done by the library had involved surveys handed out to patrons, analysis of log files, and the use of counters. (Fang 2007) The author changed methods because of some inherent flaws in using those methods to analyze website needs. They used Google Analytics in order to track user activity on the library’s website. The library “found out how many users were accurately following the path we had designed to reach a target page.” (Fang 2007) This sort of path following navigation was one of the goals they had when they designed their site. They also found out that “Visitor Segmentation showed that 83% of visitors were coming from the United States. About 50% of U.S. visitors were from New Jersey, and 76% of these were from Belleville and Newark. These results matched our predictions for patrons’ geographical patterns.” (Fang 2007) The results of this analysis enabled the library to make changes to improve their website.

This type of methodology lends itself more to a case study approach and often requires the consent of the website involved in order to get private logs. It can be used in conjunction with other methods, but should be used in a more targeted analysis of highly specific research areas.

Sentiment analysis and reputation management (COMPLETE)

Sentiment analysis involves identifying content related to a topic and identifying the emotion connected to that content. In a sport context, sentiment analysis could involve determining if newspapers are providing positive or negative coverage of a team. In a social media context, sentiment analysis would involve determining attitudes being expressed on Twitter in individual tweets. Reputation management goes one further: After sentiment has been determined, a decision needs to be made on if and how negative and positive sentiment content should be responded to. Sentiment analysis is passive analysis where non-stakeholders can conduct analysis. Reputation management is active analysis that is primarily conducted by stakeholders as part of on going activities to improve a brand, be it personal or corporate.

While sentiment analysis and reputation management are similar in their desire to monitor a response to a situation, the tools available vary differently for each type. There a variety of different tools for sentiment analysis. One of the tools for conducting sentiment analysis are freely available lists of words “that evoke positive or negative associations.” (Wanner et al. 2009) Sterne (2010) suggests that content being ReTweeted on Twitter can be seen as a tool to measure positive sentiment. Sterne (2010) suggests that the ratio of follows/followers is not an effective tool for measuring sentiment on Twitter. Reputation management tools include Trackur. It allows you to “set up searchers and the system automatically monitors the Web for key words that appear on news sites, blogs, and other social media.” (Weber 2009)

Wanner et al. (2009) did a sentiment analysis of RSS feeds that focused on the 2008 United States presidential elections. They selected 50 feeds connected to the elections and collected updates to these feeds every 30 minutes for one-month starting 9 October 2008. For each item they collected off the feeds, they also recorded the date, title, description and feed id. (Wanner et al. 2009) After that, they eliminated all noise, which mostly consisted of non-content like URLS. (Wanner et al. 2009) The next step was to filter out all items that did not contain one of the following terms: “Obama”, “McCain”, “Biden”, “Palin”, “Democrat” and “Republican”. (Wanner et al. 2009) Sentiment was then analyzed using freely available lists “that evoke positive or negative associations.” (Wanner et al. 2009) The results were then visualized. Five events that happened during this period were chosen for a more detailed visual examination. They found that the news regarding possible abuse of power by Sarah Palin in Alaska resulted in many negative posts. They also found that the debates resulted in low sentiment scores for both candidates as the candidates attacked each other. The authors concluded that the visual tool they created would be useful for monitoring public debates.

This methodology can overlap with influencer identification (Weber 2009) as part of reputation management involves determining which people are worth responding to. It can also overlap with psychographics. Despite the obvious overlaps, this type of research often appears independently and not as part of a larger study.

Content analysis,

Content analysis involves looking at the individual components of something larger and analyzing it. In a social media context, the content could be comments on a Facebook fanpage, or all the tweets made by a person or group.

With content analysis, the researcher views data as “data as representation not of physical events but of texts, images and expressions that are created to be seen, read, interpreted, and acted on for their meanings, and therefore be analyzed with such uses in mind.” (Krippendorff 2007) Krippendorff (2007) defines the basic methodology used in content analysis as unitizing, sampling, recording, reducing, inferring, and narrating.

Usability studies,

According to Sweeney, Dorey and MacLellan (2006), one of the purposes of a usability study is “point out specific usability problems with your Web site interface in line with how well your Web site speaks to your audience and their goals.”

Usability studies can be done in conjunction with traffic analysis and search analytics as the purposes are often similar: Improve the user experience and try to get users to complete certain tasks.

Interaction and collaboration analysis,

Viégas et al. (2007) did a collaboration analysis focusing on Wikipedia. The purpose of their work was to examine historical editing patterns and how editing practices have evolved over time. They built on work done by Viégas, Wattenberg and Dave in 2003. The methodology they used involved getting the editing history of articles across several different Wikipedia namespaces. The history of the articles was then examined using several visualization tools, metrics and methods depending on the established cultural practices for that namespace. One tool they used was a history flow visualization application. A method they used was the manual classification of “all user posts in a purposeful sample”. (Viégas et al., 2007) Metrics they used included count of horizontal rules, signed user names, new indentations levels, votes in polls and total “references to internal Wikipedia resources.” (Viégas et al., 2007) These tools, metrics and methods allowed them to examine how collaboration and interaction had changed over time.

Relationship analysis (COMPLETE)

Relationship analysis involves examining the relationships between users on a social network, message board or mailing list. The goal is to identify cliques of different sizes or people who are particularly influential in a particular group online. This type of analysis is important to many brands including Starbucks (Plimsoll, 2010). The purpose of relationship analysis is to identify key influencers and social who influencers who are or who have the potential to be brand evangelists. (Plimsoll, 2010)

Lord and Singh (2010) define social media influence marketing as being “about recognizing, accounting and tapping into the fact that as your potential consumer makes a purchasing decision, he or she is being influenced by different circles of people through conversations with them, both online and off.”

The methodology for influence identification is not clearly spelled out as identifying influencers can be heavily dependent on the network being examined and how the community on a specific site functions. As a result, social media marketers suggest an array of tools like Twitalyzer that can be used to help determine your own influence. (Ankeny 2009) Twitalyzer’s Peterson and Katz (2010) explain their site-specific method of determining influence as including the following variables: Engagement level, total followers, total following, hashtags cited, lists included on, frequency of updates, references by others, references of others, times content is retweeted, urls cited and a number of other variables. Sterne (2010) suggests using WeFollow.com to find people who use topic specific #hashtags on Twitter. The people who tweet the most about a topic are likely to be influencers in that others looking for tweets around a topic are likely to read them. In a wider web context, Sterne (2010) suggests using Technorati to identify bloggers who have clout and influence around a certain topic.

This type of research can be viewed as a fundamental component to sentiment analysis; social media marketing companies like Razorfish often package the two together. (Lord & Singh, 2010)

Population /Demographic studies (COMPLETE)

Population studies involve defining the demographic characteristics of a community. In a population study, the goal is also to define the limits and size of the community that is being studied. Because of the complexity in defining the boundaries of a population and in sampling the whole of it, this type of research is rarely done in terms of social media.

Daugherty and Kammeyer (1995) define a population study as the assembling “of numerical data on the sizes of populations.” This sort of data is defined by the authors as “descriptive demographic statistics.” Daugherty and Kammeyer (1995) say “population numbers are always changing, so even if they are accurate when gathered they are soon out of date and inaccurate.” Daugherty and Kammeyer (1995) say the basic purpose of conducting a demographic study “is to explain or predict changes or variations in the population variables or characteristics.” Given the definition of a population study, the methodology involves counting all members of a select population.

The most famous example of a population study is the census. In the United States, this is done every ten years. According to the U.S. Census Bureau (n.d), the goal of the 2010 US census is ” to count all U.S. residents—citizens and non-citizens alike.” This is done by sending all citizens a ten-question questionnaire, requiring that people complete it by law and having a census taker follow up for all households did not return completed questionnaires. (U.S. Census Bureau, n.d.) The results are then calculated and are used by the government to make decisions.

This type of research often stands on its own. The results will often be utilized for marketing purposes in conducting other research, such as psychographics, to make that that sampling contains representative populations.

Online target analysis of behavior and psychographics (COMPLETE)

Online targeting of and marketing towards a specific audience because of their demographic characteristics is extremely common on the Internet. Psychographics is a term that includes targeting towards a specific demographic group except it includes the offline component.

Sutherland and Canwell (2004) define psychographics as “market research and market segmentation technique used to measure lifestyles and to develop lifestyle classifications.” (p. 247) Nicolas (2009) defines online behaviorial analysis as a series of steps: Collecting user data across several sites, organizing information about users based on the sites they visit and their behavior on those sites, “infer demographics and interest data”, and classifying new users based on the collected data in order to deliver relevant ads and content based their demographic profiles. Kinney, McDaniel, and DeGaris (2008) define psychographics as attitude towards something such as a brand or involvement with an organization.

Given the methodology involved, much of this type of research involves action research in that it is done in a specific content, based on internal models to address specific situations.

An example of this type of research was done by Kinney, McDaniel, and DeGaris (2008) who investigated the demographic characteristics of NASCAR fans and their attitudes towards NASCAR, its sponsors and sponsor involvement with NASCAR. The research found that age, gender and education were all important variables in determining sponsor recall: Younger, more educated males had the best brand recall amongst NASCAR fans.

This type of research can be viewed as a subcomponent of a population study in that demographic information is sought about the population. In an online context, it often works in conjunction with search and traffic analytics analysis, content analysis, and interaction and collaboration analysis.

Predictive analysis (COMPLETE)

A search on 13 July 2010 on SPORTDiscus had three results for “predictive analysis.” A search on the same date on Scopus had 605 results, 275 of which were in engineering, 132 in computer science and 102 in medicine. Predictive analysis is probably one of the least used analysis methods, especially in social media and fandom.

What is predictive analysis? At its simplest, it is identifying a future event or events, monitoring selection actions that precede the event and seeing if those events can be used to predict the outcome of similar events in the future. If a predictive value is found, an organization can monitor behaviors to help make more informed decisions.

An example of this type of research is “Predicting the Future With Social Media” by Asur and Huberman (2010). Their goal was to determine if tweet volume and sentiment on Twitter prior to a movie being released could be used to predict how well a movie performs at the box office. Their methodology involved identifying movie wider release dates that took place on a Friday, creating a list of keyword searches related to those movies, and using the Twitter API to collect all tweets and aggregate date that mention those keywords over a three month time period. The authors then compared the tweet volume to box office performance. They concluded that social media “can be used to build a powerful model for predicting movie box-office revenue.” (Asur & Huberman, 2010)

This type of research can be used in conjunction with other methods. It can be used along side a population study to see if certain actions will result in demographic changes.
Ankeny, J. (2009). HOW TWITTER IS REVOLUTIONIZING BUSINESS. Entrepreneur, 37(12), 26-32. Retrieved from Business Source Premier database.

Asur, S., & Huberman, B. A. (2010). Predicting the Future With Social Media. Social Computing Lab. Retrieved from http://www.hpl.hp.com/research/scl/papers/socialmedia/socialmedia.pdf

Bronwyn, B., Dawson, P., Devine, K., Hannum, C., Hill, S., Leydens, J., Matuskevich, D. Traver, C, and Palmquist, M. (2005). Case Studies. Writing@CSU. Colorado State University Department of English. Retrieved August 26, 2010 from http://writing.colostate.edu/guides/research/casestudy/.

Daugherty, H. G., & Kammeyer, K. C. W. (1995). An introduction to population. New York: Guilford Press.

Fang, W. (2007), Using Google Analytics for improving library website content and design: A case study. Library Philosophy and Practice 2007 (June), LPP Special Issue on Libraries and Google. Retrieved September 2, 2010 from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.84.5924&rep=rep1&type=pdf

Jansen, B. J. (2009). Understanding user-Web interactions via Web analytics. San Rafael, Calif.: Morgan & Claypool Publishers.

Kaushik, A. (2010). Web analytics 2.0: The art of online accountability & science of customer centricity. Hoboken, N.J: Wiley.

Kinney, L., McDaniel, S., & DeGaris, L. (2008). Demographic and psychographic variables predicting NASCAR sponsor brand recall. International Journal of Sports Marketing & Sponsorship, 9(3), 169-179. Retrieved from SPORTDiscus with Full Text database.

Krippendorff, K. (2007). Content analysis: An introduction to its methodology. Thousand Oaks, Calif.

Lord, B., & Singh, S. (2010). Fluent: The Razorfish Social Influence Marketing Report. Razorfish. Retrieved August 25, 2010, from http://fluent.razorfish.com/publication/?m=6540&l=1

Nicolas, P. (2009, December 17). “Online audience behavior analysis and targeting.” Patrick Nicolas Official Home Page. Retrieved August 1, 2010, from http://www.pnexpert.com/Analytics.html

Peterson, E., & Katz, J. (2010). Twitalyzer Help and Company Information | Twitalyzer: Serious Analytics for Social Media and Social CRM. Twitalyzer. Retrieved August 25, 2010, from http://www.twitalyzer.com/help.asp

Plimsoll, S. (2010). Find and target customers in the social media maze. Marketing (00253650), 10-11. Retrieved from Business Source Premier database.

Ramos, A., & Cota, S. (2009). Search engine marketing. New York: McGraw-Hill.

Sterne, J. (2010). Social media metrics: How to measure and optimize your marketing investment. Hoboken, N.J: John Wiley.

Sutherland, J., & Canwell, D. (204). Key Concepts in Marketing. Palgrave Key Concepts. Hampshire, England: Palgrave MacMillan.

Sweeney, S., Dorey, E., & MacLellan, A. (2006). 3G marketing on the internet: Third generation internet marketing strategies for online success. Gulf Breeze, FL: Maximum Press.

Viégas, F. B., Wattenberg, M., Kriss, J., & van Ham, F. (2007). Talk Before You Type: Coordination in Wikipedia. Proceedings of the 40th Hawaii International Conference on System Sciences. Big Island, Hawaii. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.84.6907&rep=rep1&type=pdf

Vincenzini, A. (2010, July 14). A Case Study: The NBA’s Social Media Strategy & Tactics. Retrieved August 26, 2010, from http://www.slideshare.net/AdamVincenzini/the-nba-and-social-media-a-case-study

U.S. Census Bureau. (n.d.). How We Count America – 2010 Census. U.S. Census Bureau. Retrieved August 28, 2010, from http://2010.census.gov/2010census/how/how-we-count.php

Wanner, F., Rohrdantz, C., Mansmann, F., Oelke, D. and Keim, D., Visual Sentiment Analysis of RSS News Feeds Featuring the US Presidential Election in 2008. in Workshop on Visual Interfaces to the Social and Semantic Web, (2009).

Weber, L. (2009). Sticks and Stones: How Digital Business Reputations Are Created over Time and Lost in a Click. John Wiley & Sons Inc.

Related Posts:

Location, location, location: Why can’t Twitter users standardize their locations?

Posted by Laura on Friday, 27 August, 2010

If you look at Twitter related maps, you may think that they seem really sparse and that a lot more people from an area actually tweet about a topic or are from an area than actually appear on that map.  Okay, you may not notice that but I certainly do.  I like to put things on maps.  I like to be able to go : This fan of [TEAM Y] lives in [CITY, STATE, COUNTRY].  I like then to count them all and see if there isn’t a pattern to where people are from.  This just really isn’t very easy to do for a variety of reasons.  The major one is that user inputted locations are not standardized on Twitter.  Do you know how many ways there are to say you’re from Melbourne, Victoria, Australia?  I’ve found 140 variants.  I’m mostly looking for Australians and I’ve still found 53 different ways to say you’re from New York City.  The variations come from spelling, capitalization, spacing, inclusion of country or state, order of city state and country, and expression of feelings regarding the city (i luv melb.au!) are just a few.

With a small data set of around 100, it is easy enough to convert the location over to city, state, country.  When you’re talking about a data set of 2,000 to 100,000, doing it by hand becomes unfeasible.  What needs to be done is to develop a basic user input location that can be compared to a city, state, country list.  This needs to be coupled with fuzzy logic so that things like spaces and commas and capitalization are ignored.  More logic needs to be added to look for states and countries to see if the program can’t “guess” the city if the other two are known.  At the heart of that though, you still need a list for things like “Lurking…in Newcastle, NSW” and “I Love San Diego” and “Down under, mate!” and “um caitlins house :D ” to be converted to actual locations or an expression that says “I have no clue where this person lives.”

What I’ve been doing is compiling a list like that. I haven’t seen one yet.  (I might not be looking in the right places.)  My list tends to assume Australia centricism as I’m pulling from sources that should be more Australian.  (I’m looking at who follows Australian sport teams and who tweets using Australian politic hashtags.)  I thought the list I developed so far might be interesting and/or useful to others doing any sort of Twitter location related research.  If there are any on this list that you don’t think are quite right or should be something different, let me know.  Ditto that if you want a copy of this in Excel or csv.   The list is copy and pasted below.

If users could standardize their locations life would be much easier. ;)

Location City State Country
8,627     Unknown
 Brazil     Brazil
118 W Rd Bassendean WA 6054 Bassendean Western Australia Australia
2-4 Mount St Prahran VIC 3181 Prahran Victoria Australia
-33.869160,151.093704 Strathfield New South Wales Australia
8 Babra Ct Grovedale VIC 3216 Grovedale Victoria Australia
Albury Albury New South Wales Australia
Australia     Australia
Brisbane, Australia Brisbane Queensland Australia
Daytona Beach, FL Daytona Beach Florida United States
East Coast Of USA     United States
FT. Wayne IN Fort Wayne Indiana United States
Longstanton, Cambridgeshire     United Kingdom
Melbourne Melbourne Victoria Australia
Melbourne Australia Melbourne Victoria Australia
Minnesota   Minnesota United States
Montana   Montana United States
NY/LA New York City New York United States
PA USA   Pennsylvania United States
Paris Paris   France
Phoenix, Arizona Phoenix Arizona United States
Queensland, Australia   Queensland Australia
Texas   Texas United States
USA     United States
Wisconsin   Wisconsin United States
(Inner West) Sydney, AU Burwood New South Wales Australia
,{4É0¤0Ä0ýV     United States
;__;-lookin 4 my jorts(help)     Unknown
@TheCodySimpson’s heart:3 e&     Unknown
[1999] ACTSC 13   Australian Capital Territory Australia
[1999] ACTSC 13     Unknown
\/\/\ & &treasure lies here & &/\/     Unknown
| Melbourne Melbourne Victoria Australia
~WORLDWIDE~     Unknown
„0~0K0R0     Unknown
1 Infinite Loop, Cupertino Cupertino California United States
10 Downing Street, London London England United Kingdom
101 Keys Road Moorabbin Vic Au Moorabbin Victoria Australia
-14.864931,-40.842104 São Paulo   Brazil
151 Third Street     United States
17 Gough Square, London London England United Kingdom
191-195 Ryrie St, Geelong Geelong Victoria Australia
21.3069444, -157.8583333 Honolulu Hawaii United States
-23.525317,-46.661808 São Paulo   Brazil
-26.1319932851, 28.232222890 Kempton Park   South Africa
-26.1319932851, 28.232222890′ Kempton Park   South Africa
-26.804636, 153.130349 Caloundra Queensland Australia
-27.46758, 153.027892 Brisbane Queensland Australia
27.949436, -82.4651441 Tampa Florida United States
-28.00228, 153.431052 Surfers Paradise Queensland Australia
29.9647222, -90.0705556 New Orleans Louisiana United States
30 centres across every State     Unknown
30,000 feet up in the air     Unknown
30.260645,-97.732994 Austin Texas United States
31.844766, -102.473577 Odessa Texas United States
-32.011894,115.854774 Como Western Australia Australia
32.38504,-83.647406 Elko Georgia United States
32.802955, -96.769923 Dallas Texas United States
-32.926357, 151.78122 Newcastle New South Wales Australia
-32.934679,151.720119 Adamstown New South Wales Australia
-32.959927,151.685676 Charlestown New South Wales Australia
-33.505427,151.329439 Blackwall New South Wales Australia
33.660297, -117.9992265 Huntington Beach California United States
-33.720204,151.119777 Wahroonga New South Wales Australia
-33.748827,150.957884 Bella Vista New South Wales Australia
-33.806704,151.199269 Willoughby New South Wales Australia
-33.847943,151.17126 Rozelle New South Wales Australia
-33.847943,151.17126 Woolwich New South Wales Australia
-33.867139, 151.207114 Sydney New South Wales Australia
34.135087,-118.342284 Los Angeles CA United States
-34.92577, 138.599732 Adelaide South Australia Australia
35,000 feet     Unknown
35.01706319,-116.5137913 Newberry Springs CA United States
-35.2493, 149.139 Dickson Australian Capital Territory Australia
35.359329, 139.329485   Kanagawa Prefecture Japan
35.812957,-78.541187 Raleigh NC United States
-36.082137, 146.910174 Albury New South Wales Australia
-36.119016,146.853882 West Wodonga Victoria Australia
37 Albert Street, Footscray Footscray Victoria Australia
-37.34259,144.22628 Wheatsheaf Victoria Australia
37.421912,-122.087567 Mountain View CA United States
-37.563318, 143.863715 Ballarat Victoria Australia
-37.660314, 144.441634 Darley Victoria Australia
37.760571,-122.387567 San Francisco CA United States
-37.764829, 144.943778 Brunswick West Victoria Australia
-37.775544,144.935646 Ascot Vale Victoria Australia
-37.813179,144.963226 Melbourne Victoria Australia
-37.814251, 144.963169 Melbourne Victoria Australia
-37.816194,144.967461 Melbourne Victoria Australia
-37.844503,144.944308 Albert Park Victoria Australia
-37.909034,144.992161 Brighton Victoria Australia
-37.933497, 145.038504 Moorabbin Victoria Australia
-37.976293, 145.257787 Endeavour Hills Victoria Australia
-38.11122, 145.268832 Cranbourne Victoria Australia
-38.14729, 144.360735 Geelong Victoria Australia
-38.156538, 145.123524 Frankston South Victoria Australia
-38.351032, 141.587701 Portland Victoria Australia
40.039966,-75.673112 Downingtown PA United States
40.729584, -73.989487 New York New York United States
42 Wallaby Way, Sydney Sydney New South Wales Australia
43.8041334, -120.5542012 Houma Oregon United States
440!     Unknown
47.6062095, -122.3320708 Seattle Washington United States
48.864439,2.344349 Paris Ile-de-France France
48th and 6th New York City New York United States
50 United States of America     United States
53.236057, -2.306871 Twemlow England United Kingdom
602 Baby     Unknown
6th Congressional District, MD   Maryland United States
A comfortable home in Perth Perth Western Australia Australia
A Green and Pleasant Land     Unknown
A library in Central New York   New York United States
A library in Central New York     United States
A little patch in Sydney Sydney New South Wales Australia
Abbotsford, BC, Canada Abbotsford British Columbia Canada
Abbott, Texas Abbott Texas United States
ABC News, Hobart Hobart Tasmania Australia
Abilene, Texas Abilene Texas United States
above ground     Unknown
Above the sea of fog     Unknown
Abu Dhabi Abu Dhabi   United Arab Emirates
ACORN Covert Operations Center     United States
Across regoinal Victoria   Victoria Australia
ACT Canberra Canberra Australian Capital Territory Australia
ACT, Australia Canberra Australian Capital Territory Australia
Acton Park, Tasmania AU Acton Park Tasmania Australia
Adelaide Adelaide South Australia Australia
Adelaide Adelaide South Australia Australia
Adelaide – Australia Adelaide South Australia Australia
Adelaide | Australia Adelaide South Australia Australia
Adelaide Australia Adelaide South Australia Australia
adelaide sa Adelaide South Australia Australia
Adelaide South Australia Adelaide South Australia Australia
Adelaide, AU Adelaide South Australia Australia
Adelaide, Aus. Adelaide South Australia Australia
Adelaide, Australia Adelaide South Australia Australia
Adelaide, Outerspace Adelaide South Australia Australia
Adelaide, SA Adelaide South Australia Australia
Adelaide, SA, Australia Adelaide South Australia Australia
Adelaide, South Australia Adelaide South Australia Australia
Afghanistan     Afghanistan
AFLPA – Melbourne Victoria Melbourne Victoria Australia
Africa     South Africa
Africa, Asia & Central America     Panama
Agoura Hills, Ca Agoura Hills California United States
Aireys Inlet Aireys Inlet Victoria Australia
Alabama   Alabama United States
Alabama, USA   Alabama United States
Alamo City     United States
Alaska   Alaska United States
Albany, Georgia Albany Georgia United States
Albany, New York Area Albany New York United States
Albert Park Melbourne Victoria Albert Park Victoria Australia
Alberton, Adelaide, Australia Alberton South Australia Australia
Albuquerque Albuquerque New Mexico United States
Albuquerque SF LA SD LV Boise Albuquerque New Mexico United States
Albuquerque, NM Albuquerque New Mexico United States
Albury Wodonga Albury New South Wales Australia
Albury, Australia Albury New South Wales Australia
Albury, NSW Albury New South Wales Australia
Albury, NSW (temporarily) Albury New South Wales Australia
Aldie, VA Aldie Virginia United States
Alexandria, VA Alexandria Virginia United States
Algonquin, IL Algonquin Illinois United States
Alice Springs NT Alice Springs Northern Territory Australia
All over the world     Unknown
Allegan County, MI Allegan County Michigan United States
Allentown, PA Allentown Pennsylvania United States
Almost 30 Seconds To Mars     United States
Alphen a/d Rijn | Melbourne Melbourne Victoria Australia
Al’s toy barn.     Unknown
Always Traveling..     Unknown
Amarillo, Texas Amarillo Texas United States
America     United States
America     United States
America the Beautiful     United States
AMERICA, FREE & BRAVE 4EVER     United States
America, the one & only.     United States
AMERICAN Midwest   Ohio United States
America’s Backyard     United States
Amsterdam Amsterdam   Netherlands
Amsterdam via Melbourne Amsterdam   Netherlands
Amsterdam, Netherlands Amstersdam   Netherlands
An Aussie living in Frankfurt Frankfurt   Germany
Anchorage, Alaska Anchorage Alaska United States
Ann Arbor, MI Ann Arbor Michigan United States
Anti-Liberal, USA     United States
Antwerp, Belgium Antwerp   Belgium
anywhere     Unknown
Anywhere can go GREEN!     Unknown
anywhere with cell service     Unknown
Anywhere you need us     Unknown
ANYWHERE YOU WANT ME BABE ;P     Unknown
Anywhere you want me to be     Unknown
Anywhere, USA     United States
Apollo Bay Victoria Apollo Bay Victoria Australia
Apollo Bay, Australia Apollo Bay Victoria Australia
apollo bay, vic, australia Apollo Bay Victoria Australia
Apopka, Florida Apopka Florida United States
Appalachia   Pennsylvania United States
Arden Street, North Melbourne North Melbourne Victoria Australia
Arizona   Arizona United States
Arizona (my heart is)   Arizona United States
Arizona California Chicago Chicago Illinois United States
Arizona – USA   Arizona United States
ARIZONA !!!!   Arizona United States
Arizona-USA   Arizona United States
Arkansas   Arkansas United States
Arlington, Texas Arlington Texas United States
Arlington, VA Arlington Virginia United States
Arlington, Va. Arlington Virginia United States
Arlington, Virginia Arlington Virginia United States
Arnold’s Kalifornia   California United States
Around here somewhere…     Unknown
Around the World     Unknown
Around the world.     Unknown
Arse end of the world     Australia
Arundel, Brighton, Sussex, UK Brighton England United Kingdom
Asheville, NC, USA Asheville North Carolina United States
At the MCG Melbourne Victoria Australia
At the races     Unknown
At the track. And on the punt.     Unknown
Athens, Greece Athens   Greece
ATL Atlanta Georgia United States
ATL GA Atlanta Georgia United States
Atlanta Atlanta Georgia United States
Atlanta and some other places Atlanta Georgia United States
Atlanta by way Florida     United States
Atlanta GA Atlanta Georgia United States
Atlanta, GA Atlanta Georgia United States
Atlanta, GA USA – Worldwide     United States
Atlanta, Ga. Atlanta Georgia United States
Atlanta, GA—Vh1!!!     United States
Atlanta, Georgia Atlanta Georgia United States
Atlanta, Georgia, USA Atlanta Georgia United States
Atlanta, NYC, LA… Atlanta Georgia United States
Atlantic City, NJ Atlantic City New Jersey United States
AU     Australia
Auckland, Auckland Auckland New Zealand
Auckland, New Zealand Auckland Auckland New Zealand
Augusta, Georgia Augusta Georgia United States
Aurora, Colorado Aurora Colorado United States
Aus.     Australia
Aus; Qld, out the back..   Queensland Australia
AUSSIE!!!     Australia
Ausssie aussie aussie (:     Australia
Austin Austin Texas United States
Austin tx Austin Texas United States
Austin, Texas Austin Texas United States
Austin, TX Austin Texas United States
Austraila     Australia
Australia     Australia
Australia & New Zealand     Australia
Australia & ROWorld     Australia
Australia (Melbourne HQ) Melbourne Victoria Australia
australia .&     Australia
Australia / Gold Coast /Sydney Gold Coast Queensland Australia
Australia :)     Australia
australia = melbourne Melbourne Victoria Australia
Australia 2444 Port Macquarie New South Wales Australia
Australia and all over     Australia
Australia Bitchss     Australia
Australia Felix.     Australia
Australia Melbourne Melbourne Victoria Australia
Australia SA Renmark Renmark South Australia Australia
Australia Side     Australia
Australia!     Australia
AUSTRALIA!!!     Australia
Australia, Adelaide Adelaide South Australia Australia
Australia, Brisbane Brisbane Queensland Australia
Australia, Ettalong Beach, NSW Ettalong Beach New South Wales Australia
australia, melbourne Melbourne Victoria Australia
Australia, NSW.   New South Wales Australia
AUSTRALIA, sydney Sydney New South Wales Australia
Australia, Victoria, Geelong Geelong Victoria Australia
Australia, Victoria, Melbourne Melbourne Victoria Australia
Australia,, land down under.     Australia
Australia,Planet Earth.     Australia
Australia. geelong Geelong Victoria Australia
australia..     Australia
australia…perth Perth Western Australia Australia
Australia/Geelong Geelong Victoria Australia
Australia/International     Australia
Australia/Los Angeles Los Angeles California United States
Australia/Melbourne Melbourne Victoria Australia
Australia/Philippines     Australia
Australia/Victoria/Geelong Geelong Victoria Australia
australiaa e&     Australia
Australiaaaa     Australia
Australind, Australia     Australia
Av Getúlio Vargas, 1423, BH/MG Av Getúlio Vargas   Brazil
Away From You ;]     Unknown
Ayrshire, Scotland, UK Ayrshire Scotland United Kingdom
AZ, USA   Arizona United States
B/ford, VIC, AUSTRALIA!!!!! Batesford Victoria Australia
badelaide Adelaide South Australia Australia
Bakersfield, CA Bakersfield California United States
Balaclava, Melbourne Balaclava Victoria Australia
Baldwinsville, NY Baldwinsville New York United States
Bali, Indonesia Bali   Indonesia
Ballarat Ballarat Victoria Australia
Ballarat Australia Ballarat Victoria Australia
Ballarat, Australia Ballarat Victoria Australia
Ballarat, VIC, Australia Ballarat Victoria Australia
Ballarat, Victoria Ballarat Victoria Australia
Ballarat, victoria australia Ballarat Victoria Australia
Ballarat, Victoria, Australia Ballarat Victoria Australia
Ballarat,Victoria, Australia Ballarat Victoria Australia
Ballarat/Seymour, Australia Ballarat Victoria Australia
Baltimore Baltimore Maryland United States
Baltimore, MD Baltimore Maryland United States
Bangalore Bangalore Karnataka India
Bangalore, India Bangalore   India
Bangalow Australia Bangalow New South Wales Australia
Bangkok and Melbourne Melbourne Victoria Australia
BANGKOK, HONG KONG Bangkok   Thailand
Banjarmasin, Indonesia Banjarmasin   Indonesia
Bannockburn, Vic, Australia Bannockburn Victoria Australia
Barcelona Barcelona   Spain
Barcelona, Spain Barcelona   Spain
Barnbougle Dunes, Tasmania Barnbougle Dunes Tasmania Australia
Barranquilla,Colombia Barranquilla   Colombia
Batemans Bay Batemans Bay New South Wales Australia
Bathurst, NSW Bathurst New South Wales Australia
Bauru Bauru São Paulo Brazil
Bay Area Palo Alto California United States
bay area via nyc Palo Alto California United States
Bay Area, CA San Fransisco California United States
Bay Area, California Palo Alto California United States
Baysie. Bayswater Western Australia Australia
Be Here Now     Unknown
Beantown, USA Boston Massachusetts United States
Beckley, WV, USA Beckley West Virginia United States
Beechworth, VIC, Australia. Beechworth Victoria Australia
Behind The Bar     Australia
Behind The Obama Curtain     United States
Beijing Beijing   China
Beijing, China Beijing   China
Beijing,China Beijing   China
Belfast, Northern Ireland Belfast Northern Ireland United Kingdom
Belgium     Belgium
Bellarine, Melbourne Bellarine Victoria Australia
Belleville, Ontario, Canada Belleville Ontario Canada
Bellevue, WA Bellevue Washington United States
Bellingen, NSW Australia Bellingen New South Wales Australia
bellingham, washington Bellingham Washington United States
Belo Horizonte Belo Horizonte Minas Gerais Brazil
Benalla, Victoria, Australia Benalla Victoria Australia
Bendigo Australia Bendigo Victoria Australia
Bendigo, AUS Bendigo Victoria Australia
Bendigo, Australia Bendigo Victoria Australia
Bergen in Norway Bergen   Norway
Berkeley Berkeley California United States
Berkeley or Paris Berkeley California United States
Berkeley, CA Berkeley California United States
Berkeley, California Berkeley California United States
Berkeley, SF, LA Berkeley California United States
Berlin Berlin   Germany
Berlin, Germany Berlin   Germany
Berwick Berwick Victoria Australia
Best Damn Country on Earth-USA     United States
Bethlehem, PA Bethlehem Pennsylvania United States
Between Keyboard & Melbourne. Melbourne Victoria Australia
Between mantle & stratosphere.     Unknown
Beverly Hills, CA Beverly Hills California United States
Beverly Hills, FL Beverly Hills Florida United States
Bexley North, NSW, Australia Bexley North New South Wales Australia
biboohra Cairns Australia Biboohra Queensland Australia
biboohra Cairns Australia Cairns Queensland Australia
Big Town Perth, Australia Perth Western Australia Australia
Birm. AL and NYC Birmingham Alabama United States
Birmingham / London , England London England United Kingdom
Birmingham, UK Birmingham England United Kingdom
Birsbane, Australia Brisbane Queensland Australia
Blowing the Regressives away     United States
Blue Mountains Blue Mountains New South Wales Australia
Blue Mountains NSW Australia Blue Mountains New South Wales Australia
Blue Mountains, Australia Blue Mountains New South Wales Australia
Blue Mountains, NSW, Australia Blue Mountains New South Wales Australia
Blue Mountains, Ontario Blue Mountains Ontario Canada
Body? L.A. Mind? N.J. Newark New Jersey United States
Bogart, GA Bogart Georgia United States
Bondi Beach, Australia Bondi Beach New South Wales Australia
Bondi Beach; Sydney; Australia Bondi Beach New South Wales Australia
Bondi, Australialand Bondi New South Wales Australia
Boronia, Vic, Oz! Boronia Victoria Australia
BOS, NYC, CHI, SFO, LHR Boston Massachusetts United States
Boston Boston Massachusetts United States
Boston (Eastie) Boston Massachusetts United States
Boston via East Milton Boston Massachusetts United States
Boston, MA Boston Massachusetts United States
Boston, Ma Boston Massachusetts United States
Boston, MA/Naples, FL Boston Massachusetts United States
Boston, Montreal, NYC Boston Massachusetts United States
Boston, Taxachusetts Boston Massachusetts United States
Boston,Massachusetts Boston Massachusetts United States
Boston/Concord/Outside Boston Massachusetts United States
Boulder Boulder Colorado United States
Boulder CO Boulder Colorado United States
Boulder, CO Boulder Colorado United States
Boulder, CO USA     United States
Boulder, Colorado Boulder Colorado United States
Bowen Hills, Queensland Bowen Hills Queensland Australia
Bowral, NSW Bowral New South Wales Australia
Boxford Massachusetts Boxford Massachusetts United States
Boxford, MA Boxford Massachusetts United States
Brackley, Northants, UK Brackley England United Kingdom
Bradenton Florida Bradenton Florida United States
Brazil     Brazil
Breamlea, Australia Breamlea Victoria Australia
Bregenz, Austria Bregenz   Austria
Brekky Central     Unknown
BRICK CITY Newark New Jersey United States
Brisbane Brisbane Queensland Australia
Brisbane – Australia Brisbane Queensland Australia
Brisbane Australia Brisbane Queensland Australia
Brisbane for now Brisbane Queensland Australia
Brisbane Qld, Australia Brisbane Queensland Australia
Brisbane Queensland Brisbane Queensland Australia
Brisbane Queensland Australia Brisbane Queensland Australia
Brisbane Queensland. Brisbane Queensland Australia
Brisbane, AU Brisbane Queensland Australia
Brisbane, AUS Brisbane Queensland Australia
Brisbane, Australia Brisbane Queensland Australia
Brisbane, Australia Brisbane Queensland Australia
Brisbane, Australia via NZ Brisbane Queensland Australia
Brisbane, Australia! Brisbane Queensland Australia
Brisbane, Australia. Brisbane Queensland Australia
Brisbane, duh. Brisbane Queensland Australia
Brisbane, from my house. Brisbane Queensland Australia
Brisbane, Melbourne, Sydney Brisbane Queensland Australia
Brisbane, QLD Brisbane Queensland Australia
Brisbane, QLD Australia Brisbane Queensland Australia
Brisbane, Qld, AU Brisbane Queensland Australia
Brisbane, QLD, AUS Brisbane Queensland Australia
Brisbane, Qld, Aust Brisbane Queensland Australia
Brisbane, QLD, Australia Brisbane Queensland Australia
Brisbane, QLD. Brisbane Queensland Australia
Brisbane, Queensland Brisbane Queensland Australia
Brisbane, Queensland Australia Brisbane Queensland Australia
Brisbane, Queensland, Australi Brisbane Queensland Australia
Brisbane,Australia Brisbane Queensland Australia
Brisbane. Brisbane Queensland Australia
Brisbane/Sydney Australia Brisbane Queensland Australia
Brisbane? Sydney? Korea? Brisbane Queensland Australia
Bristol, CT Bristol Connecticut United States
Bristol, Ct. Bristol Connecticut United States
Brisvegas Brisbane Queensland Australia
brisvegas baby Brisbane Queensland Australia
Brisvegas, Australia Brisbane Queensland Australia
Brisvegas/ L.A Brisbane Queensland Australia
Broken Hill NSW Australia Broken Hill New South Wales Australia
Brooklyn Brooklyn New York United States
Brooklyn Park, Minnesota, USA Brooklyn Park Minnesota United States
Brooklyn! Brooklyn New York United States
Brooklyn, New York Brooklyn New York United States
brooklyn, new york Brooklyn New York United States
Brooklyn, New York 11203 Brooklyn New York United States
Brooklyn, NY Brooklyn New York United States
Brooklyn, NY + New York, NY Brooklyn New York United States
Brooklyn,NY Brooklyn New York United States
Brookvale, New South Wales AU Brookvale New South Wales Australia
Bruce, ACT Bruce Australian Capital Territory Australia
Brunswick Brunswick Victoria Australia
Brunswick West Brunswick West New South Wales Australia
Brunswick West Brunswick West Victoria Australia
Brunswick, ME Brunswick Maine United States
Brunswick, Melbourne Brunswick Victoria Australia
Brunswick, Victoria Brunswick Victoria Australia
Brunswickistan Brunswick Victoria Australia
Brussels, Belgium Brussels   Belgium
Bucharest, somewhere Bucharest   Romania
Buffalo, NY Buffalo New York United States
Bulimba, Brisbane Australia Bulimba Queensland Australia
Bunbury, Australia Bunbury Western Australia Australia
Bunbury, WA Bunbury Western Australia United States
Bundaberg, Australia Bundaberg Queensland Australia
bundoora, melbourne vic.   Victoria Australia
Burbank, CA Burbank California United States
Burleigh Heads QLD Burleigh Heads Queensland Australia
Burlington, Ontario Burlington Ontario Canada
Burwood, Melbourne Burwood Victoria Australia
Burwood, Sydney, Australia Burwood New South Wales Australia
Bx, Manhattan, SI Manhattan New York United States
By The Beach | Australia     Australia
By the ocean     Australia
Byron Bay Byron Bay New South Wales Australia
Byron Bay | Australia Byron Bay New South Wales Australia
Byron Bay and other Byron Bay New South Wales Australia
Byron Bay, NSW Byron Bay New South Wales Australia
CA   California United States
Caboolture, QLD, Australia Caboolture Queensland Australia
Cairns Cairns Queensland Australia
Cairns, Australia Cairns Queensland Australia
Cairns, FNQ, Australia Cairns Queensland Australia
Cairns, QLD, Australia Cairns Queensland Australia
Cairns, Queensland, Australia Cairns Queensland Australia
Caldwell, ID Caldwell Idaho United States
Calgary, Alberta Calgary Alberta Australia
Calgary, Canada, Twitterverse Calgary Alberta Canada
Cali   California United States
Cali World   California United States
California   California United States
California + ie&Parise&Rioe&HKe&NY Paris   France
California, USA   California United States
California/Texas   California United States
Caloundra,QLD(5-0), Australia Caloundra Queensland Australia
Cambridge, MA Cambridge Massachusetts United States
Cambridge, Mass. Cambridge Massachusetts United States
Cambridge, UK Cambridge England United Kingdom
Camelford, North Cornwall Camelford North Cornwall England
Camp Hill, QLD, Australia Camp Hill Queensland Australia
campbelltown Australia Campbelltown New South Wales Australia
Camperdown, Victoria Camperdown Victoria Australia
Canada     Canada
Canada’s capital Ottawa Ontario Canada
Canadian, in Broadbeach, QLD Broadbeach Queensland Australia
Canberra Canberra Australian Capital Territory Australia
Canberra Australia Canberra Australian Capital Territory Australia
Canberra – Australia Canberra Australian Capital Territory Australia
Canberra & Goulburn, Australia Goulburn New South Wales Australia
Canberra Australia Canberra Australian Capital Territory Australia
Canberra International Airport Canberra Australian Capital Territory Australia
Canberra Press Gallery Canberra Australian Capital Territory Australia
Canberra, ACT Canberra Australian Capital Territory Australia
Canberra, ACT, Australia Canberra Australian Capital Territory Australia
Canberra, Australia Canberra Australian Capital Territory Australia
Canberra, Australia. Canberra Australian Capital Territory Australia
Canberra, Australian Capital T Canberra Australian Capital Territory Australia
Canberra, CIT Canberra Australian Capital Territory Australia
Canberra, more or less. Canberra Australian Capital Territory Australia
canberra… Canberra Australian Capital Territory Australia
Canberra/Sydney Canberra Australian Capital Territory Australia
Canfield Ohio Canfield Ohio United States
Canterbury, Melbourne Canterbury Victoria Australia
Cape Cod/Canberra Canberra Australian Capital Territory Australia
Capital Federal, Argentina Capital Federal   Argentina
Capital of the 21st CenturyNYC New York City New York United States
Cardiff by the Sea, CA Cardiff by the Sea California United States
carisbrook, VIC, AUSTRALIA Carisbrook Victoria Australia
Carmel, California Carmel California United States
Cartersville, Georgia Cartersville Georgia United States
cartlon mate, australia Carlton Victoria Australia
Casterton, Victoria, Australia Casterton Victoria Australia
Castle Hill Castle Hill New South Wales Australia
Castro Valley SF Bay Area Castro Valley California United States
çay1r çimen     Unknown
Cebu City Cebu City   Philippines
Centennial Park, NSW, Australi Centennial Park New South Wales Australia
Central Arizona USA   Arizona United States
Central Australia     Australia
Central California   California United States
Central Coast NSW Gosford New South Wales Australia
Central Coast NSW Australia Gosford New South Wales Australia
Central Coast, Australia Gosford New South Wales Australia
Central Coast, New South Wales Gosford New South Wales Australia
Central Coast, NSW Australia Gosford New South Wales Australia
Central Coast, NSW, Australia Gosford New South Wales Australia
Central QlD Gladstone Queensland Australia
Central Valley, CA Central Valley California United States
Central West NSW Gosford New South Wales Australia
Central West, NSW Gosford New South Wales Australia
Champaign, IL Champaign Illinois United States
Charlottesville Charlottesville Virginia United States
Charlottesville, VA Charlottesville Virginia United States
chengdu,china ÿ–™ybÝO•     China
Chennai, India Chennai   India
Cherry Hill, NJ Cherry Hill New Jersey United States
Chiapas, Mexico Chiapas   Mexico
Chicago Chicago Illinois United States
Chicago ’ Arizona World TRAVEL Chicago Illinois United States
Chicago – Uptown Chicago Illinois United States
Chicago / L.A. Chicago Illinois United States
Chicago land, IL Chicago Illinois United States
Chicago metro area Chicago Illinois United States
Chicago, Il Chicago Illinois United States
Chicago, IL USA Chicago Illinois United States
Chicago, Illinois Chicago Illinois United States
Chicago, USA Chicago Illinois United States
Chicago/Brooklyn Chicago Illinois United States
Chicago/Washington D.C. Chicago Illinois United States
Chicagoland, Illinois Chicago Illinois United States
Chicago-Milwaukee Metroplex Chicago Illinois United States
China     China
Chirstchurch, New Zealand Christchurch   New Zealand
Chiswick,London Chiswick England United Kingdom
Chittaway Bay, NSW, Australia Chittaway Bay New South Wales Australia
Cibolo, Tx Cibolo Texas United States
Cincinnati Cincinnati Ohio United States
Clay Wells 5280 Sth Australia Clay Wells South Australia Australia
Clayfield, QLD, Australia Clayfield Queensland Australia
Clearwater, Tampa, Florida Clearwater Florida United States
Clemmons, North Carolina Clemmons North Carolina United States
Cleveland, OH Cleveland Ohio United States
CLEVELAND/EVERYWHERE Cleveland Ohio Unknown
Clifton Springs, Geelong Clifton Springs Victoria Australia
Closer than you know.     United States
Cloudy some showers Melbourne Melbourne Victoria Australia
Coaltopia, Australia Carlton Victoria Australia
Colchester, UK Colchester England United Kingdom
College Station, Tx College Station Texas United States
Collingwood, Victoria Collingwood Victoria Australia
Colorado   Colorado United States
Colorado, USA   Colorado United States
Columbia, Missouri Columbia Missouri United States
Columbia, MO Columbia Missouri United States
Columbia, Mo. Columbia Missouri United States
Columbia, South Carolina Columbia South Carolina United States
Combe Martin, UK Combe Martin England United Kingdom
Comme, 7 Alfred Pl, Melbourne Melbourne Victoria Australia
COMPTON SANITATION PLANT Compton California United States
Connecticut   Connecticut United States
Connecticut, U.S.A.   Connecticut United States
Connecticut, USA   Connecticut United States
Contented Pagan 4om d Kingdom     Unknown
Conway, AR Conway Arkansas United States
Coogee, NSW Coogee New South Wales Australia
Cook Islands.     Cook Islands
Cooktown, Qld, Australia Cooktown Queensland Australia
Coolum Beach Coolum Beach Queensland Australia
Costa Rica     Costa Rica
Costa Rica / Global     Costa Rica
Country NSW   New South Wales Australia
Country Victoria   Victoria Australia
Cowra, Australia Cowra New South Wales Australia
Crackerville, USA     United States
Cranbourne Cranbourne Victoria Australia
Cranbourne, Victoria AU Cranbourne Victoria Australia
Cremorne Sydney Cremorne New South Wales Australia
Cremorne, VIC Cremorne Victoria Australia
Culver City, CA Culver City California United States
Cuyahoga Falls, Ohio Cuyahoga Falls Ohio United States
Cydonia     Unknown
D.M.V USA     United States
D/FW, Tx Dallas Texas United States
DA VILLE ….     Unknown
Dallas Dallas Texas Australia
DALLAS TEXAS Dallas Texas United States
Dallas TX USA Dallas Texas United States
Dallas Tx. Dallas Texas United States
Dallas, Texas Dallas Texas United States
Dallas, TX Dallas Texas United States
DALLAS/LA Dallas Texas United States
Dandenong Ranges (Oz) Kilsyth Victoria Australia
Dandenong Ranges, VIC Kilsyth Victoria Australia
Darlington, Sydney Darlington New South Wales Australia
Darwin Darwin Northern Territory Australia
Darwin, Australia Darwin Northern Territory Australia
Davos, Switzerland Davos   Switzerland
Deakin University, VIC, Austra Waurn Ponds Victoria Australia
Dean, Victoria Dean Victoria Australia
Dee Why Beach, Sydney, NSW Dee Why Beach New South Wales Australia
Dee Why NSW Australia Dee Why New South Wales Australia
deep blue state     United States
Deep Cove, Vancouver, Canada Deep Cove British Columbia Canada
Deer Park, Australia Deer Park Victoria Australia
Delaware   Delaware United States
Delaware County   Delaware United States
Delhi n Punjab Delhi   India
Delhi, India Delhi   India
Deloraine, Tasmania Deloraine Tasmania Australia
Denial Island     Unknown
Deniliquin, Australia Deniliquin New South Wales Australia
Denmark, Western Australia Denmark Western Australia Australia
Denver Denver Colorado United States
Des Moines, Iowa Des Moines Iowa United States
Des Moines, Washington Des Moines Washington United States
Detroit, MI Detroit Michigan United States
Deutschland, Bayern, Hof     Germany
Devonport, Tasmania Devonport Tasmania Australia
Devonport, Tasmania (Aust) Devonport Tasmania Australia
DFW, TX Dallas Texas United States
Dirty Dirty Desert, New Mexico   New Mexico United States
District 22, Florida   Florida United States
Docklands, Melbourne Docklands Victoria Australia
Does this matter anymore?     Unknown
Dogbowl, USA     United States
Doha, Qatar Doha   Qatar
Don’t call it San Fran. San Francicso California United States
Dover, Delaware Dover Delaware United States
Down under, mate!     Australia
dreamteamtalk.com     Australia
Driving a Kawasaki 750     United States
Drouin Victoria, Australia Drouin Victoria Australia
Drysdale, Victoria Australia Drysdale Victoria Australia
Dublin Dublin Dublin Ireland
Dublin, Ireland Dublin Dublin Ireland
Dublin, Ireland. Dublin Dublin Ireland
Durban, South Africa Durban   South Africa
e& Melbourne, Australia. Melbourne Victoria Australia
Earth     Unknown
East Burwood, Australia Burwood East Victoria Australia
East Coast     United States
East Coast Australia     Australia
East Los Angeles Los Angeles California United States
East Melbourne Manningham Victoria Australia
East Melbourne, VIC (-37.81045 Maroondah Victoria Australia
East Melbourne, VIC, Australia East Melbourne Victoria Australia
East of Perth Perth Western Australia Australia
East St Kilda, OZ St. Kilda Victoria Australia
East Texas   Texas United States
East Texas Gulf Coast   Texas United States
Eastcoast, Australia     Australia
Eastern USA     United States
Edinburgh, UK Edinburgh Scotland United Kingdom
Edinburgh, UK     United Kingdom
Edmonton, Canada     Canada
Egypt     Egypt
Ellenbrook, West Australia Ellenbrook Western Australia Argentina
Elstree Elstree England United Kingdom
Encinitas, California Encinitas California United States
Endeavour hills Endeavour Hills Victoria Australia
England   England United Kingdom
ENGLAND.   England United Kingdom
España     Spain
essex Essex England United Kingdom
Etsyland…Sunny CA Etsyland California United States
Evanston, IL USA Evanston Illinois United States
Every State in the USA     United States
Everywhere     Unknown
Everywhere     Unknown
Everywhere and nowhere.     Unknown
Everywhere anytime     Unknown
EveryWhere I want     Unknown
Everywhere or Anywhere ?     Unknown
Everywhere We Are     Unknown
Everywhere You Are     Unknown
Everywhere you wanna be!     Unknown
Everywhere!     Unknown
EVERYWHERE!!!     Unknown
Everywhere, All the time!     Unknown
Everywhere/ D.C. area Washington DC   United States
Everywheree     Unknown
exactly 372 metres away     Unknown
Eywa     United States
F Town Fremantle Western Australia Australia
Fairview, TN, USA Fairview Tennessee United States
Fantasyland     Unknown
Far North Queensland Cooktown Queensland Australia
Far Northside of Brisbane.au Fortitude Valley Queensland Australia
Far West Texas   Texas United States
Federal Seat of Cowper Cowper New South Wales Australia
Finger Lakes, NY Finger Lakes New York United States
FITZROY AUSTRALIA Fitzroy Victoria Australia
Fitzroy, Melbourne, Australia Fitzroy Victoria Australia
Fl   Florida Finland
FL, USA   Florida United States
Flagstaff, Arizona Flagstaff Arizona United States
Florida – USA   Florida United States
Florida to Arizona   Florida United States
Florida, USA   Florida United States
Florida’s Gulf Coast   Florida United States
Folsom, CA Folsom California United States
Footscray Footscray Victoria Australia
Footscray (Melbourne) Footscray Victoria Australia
For mums everywhere     Unknown
Forest Lodge, New South Wales Forest Lodge New South Wales Australia
Fort Collins, Colorado, USA Fort Collins Colorado United States
Fort Lee, NJ USA Fort Lee New Jersey United States
Fort McMurray, Alberta, Canada Fort McMurray Alberta Australia
Fort Walton Beach, FL Fort Walton Beach Florida United States
Fort Worth Texas Fort Worth Texas United States
Founded in 1954 in Miami FL Miami Florida United States
Fountain Hills, Arizona Fountain Hills Arizona United States
Fountain Valley, CA Fountain Valley California United States
France, Riviera     France
Frankston South Frankston South Victoria Australia
Frankston, Australia Frankston South Victoria Australia
Freer, TX     United States
Fremantle Fremantle Western Australia Australia
Fremantle WA Fremantle Western Australia Australia
Fremantle, Australia Fremantle Western Australia Australia
Fremantle, Western Australia Fremantle Western Australia Australia
Fresno, CA Fresno California United States
fresno,ca Fresno California United States
Frisco, TX Frisco Texas United States
Ft. Lauderdale, FL USA Ft. Lauderdale Florida United States
fuckyeahdelaide! Adelaide South Australia Australia
Funkytown     Unknown
FUTURIST: visits 54 NATIONS     Unknown
Gadget HQ     Unknown
Galactic Lizard Mothership     Unknown
Galut, Israel Galut   Israel
gangsta town     Unknown
Gardena, CA, USA Gardena California United States
Gateway Business Park, Sydney Silverwater New South Wales Australia
GC Gold Coast Queensland Australia
GC Australia Gold Coast Queensland Australia
Geelong Geelong Victoria Australia
geelong & melbourne Geelong Victoria Australia
Geelong & Surrounds, VIC Geelong Victoria Australia
Geelong & the Surf Coast Torquay Victoria Australia
Geelong (: Geelong Victoria Australia
Geelong , Victoria Geelong Victoria Australia
Geelong / Stephen Frys Womb Geelong Victoria Australia
Geelong | Victoria Geelong Victoria Australia
Geelong Aust Geelong Victoria Australia
Geelong Australia Geelong Victoria Australia
Geelong ‘home of the Cats’ Geelong Victoria Australia
Geelong Vic Geelong Victoria Australia
Geelong West Geelong Victoria Australia
Geelong, Australia Geelong Victoria Australia
Geelong, Aus Geelong Victoria Australia
Geelong, Australia Geelong Victoria Australia
Geelong, Australia :) Geelong Victoria Australia
Geelong, Australia. Geelong Victoria Australia
Geelong, Melbourne, QLD Geelong Victoria Australia
Geelong, Vic, Australia Geelong Victoria Australia
Geelong, Victoria Geelong Victoria Australia
Geelong, Victoria , Australia Geelong Victoria Australia
Geelong, Victoria Australia Geelong Victoria Australia
Geelong, Victoria, Australia Geelong Victoria Australia
Geelong,Victoria Geelong Victoria Australia
geelong. Geelong Victoria Australia
geelong. Geelong Victoria Australia
Geelong/Ahmedabad Ahmedabad   India
Geelong/Australia Geelong Victoria Australia
Geelong/Torquay/Melbourne Torquay Victoria Australia
Generally, Melbourne. Melbourne Victoria Australia
Gent, Belgium Gent   Belgium
Georgetown, Demerara-Mahaica G Georgetown Demerara-Mahaica Guyana
Georgia   Georgia United States
Georgia US   Georgia United States
Georgia, USA   Georgia United States
Gig Harbor, WA Gig Harbor Washington United States
Gippsland.vic.australia. Gippsland Victoria Australia
Glebe Sydney Glebe New South Wales Australia
Glens Falls, New York Glens Falls New York United States
Global     Unknown
Global as Needed!     Unknown
Global BC NY PNG NZ CA … New York City New York United States
Global.     Unknown
GMT -5 approx 28N -81W     United States
Godrics Hollow.     Unknown
Gold Coast Gold Coast Queensland Australia
Gold Coast Australia Gold Coast Queensland Australia
Gold Coast QLD Australia Gold Coast Queensland Australia
GOLD COAST, AUS Gold Coast Queensland Australia
Gold Coast, Australia Gold Coast Queensland Australia
Gold Coast, Australia Gold Coast Queensland Australia
gold coast, australia :) ) Gold Coast Queensland Australia
Gold Coast, Australia. Gold Coast Queensland Australia
Gold Coast, Qld Gold Coast Queensland Australia
Gold Coast, QLD, Australia Gold Coast Queensland Australia
Gold Coast, Queensland Gold Coast Queensland Australia
Gold Coast,Australia Gold Coast Queensland Australia
Gold Coast,Australia QLD Gold Coast Queensland Australia
Gold Coast…I’m a gypsy. Gold Coast Queensland Australia
Golden Square, Vic., Australia Golden Square Victoria Australia
Gordon, Sydney Gordon New South Wales Australia
Gosford NSW Australia Gosford New South Wales Australia
Grand Rapids,MI Grand Rapids Michigan United States
Great Commonwealth of Virginia   Virginia United States
Great State of Texas     United States
Greece     Greece
Green Bay, NY Green Bay New York United States
Greenbay Greenbay Wisconsin United States
Greenwich Village, NYC New York City New York United States
Griffith, Australia Griffith New South Wales Australia
Gtown Geelong Victoria Australia
G-Town, Victoria Geelong Victoria Australia
Guangzhou,China Guangzhou   China
Guararapes/Ilha Solteira- SP Guararapes   Brazil
Gulf Coast Fl/Jersey Shore   Florida United States
Gulf of Mexico   Florida Mexico
Hackney, London London England United Kingdom
Hagerstown, MD Hagerstown Maryland United States
Half Moon Bay, California, USA Half Moon Bay California United States
Halfway between heaven & hell     United States
Hamilton VIC Hamilton Victoria Australia
Hamilton, NY 13346 Hamilton New York United States
Hamilton, Victoria Hamilton Victoria Australia
Hammersmith, Greater London (5 Hammersmith England United Kingdom
Hampton Roads, Va. Hampton Roads Virginia United States
Harlem, USA New York City New York United States
Hastings Point Hastings Point New South Wales Australia
Hastings Point, Australia Hastings Point New South Wales Australia
Hastings, New Zealand Hastings   New Zealand
Hastings, Victoria Hastings Victoria Australia
Hawaii   Hawaii United States
Hawaii, London & Everywhere London England United Kingdom
Hawaii, USA     United States
Headquartered in San Francisco San Francisco California United States
Healesville, Australia Healesville Victoria Australia
heart melbourne Melbourne Victoria Australia
Hell     Unknown
hell’ngone USA     United States
HelLpool, NY   New York United States
Henty, N.S.W., Australia Henty New South Wales Australia
Herald Sun, Melbourne Melbourne Victoria Australia
Here     Unknown
Here and there     Unknown
Here and there.     Unknown
Here There and Everywhere     Unknown
Here!     Unknown
Here, There & Everywhere     Unknown
Hernando, FL Hernando Florida United States
Heyfield Australia Heyfield Victoria Australia
HHI,South Carolina   South Carolina United States
Higher Realms     Unknown
Highton, VIC, Australia Highton Victoria Australia
Hmm still deciding Lol     Unknown
Hobart Hobart Tasmania Australia
Hobart slash Holland Hobart Tasmania Australia
Hobart Tasmania Australia Hobart Tasmania Australia
Hobart, Australia Hobart Tasmania Australia
Hobart, Tas, Aus Hobart Tasmania Australia
hobart, tasmania Hobart Tasmania Australia
Hobart, Tasmania, Australia Hobart Tasmania Australia
hobart,australia Hobart Tasmania Australia
Hobart,Tasmania Hobart Tasmania Australia
Hogwarts     Unknown
Hogwarts.     Unknown
Hollister, CA Hollister California United States
Hollywood Hollywood California United States
Hollywood, CA Hollywood California United States
Hollywood, CA 323-622-8612 Hollywood California United States
Hollywood, California Hollywood California United States
Holmfirth, West Yorkshire, UK Holmfirth England United Kingdom
Home     Unknown
Home is where I am at today     Unknown
Home, Where are you?     Unknown
Hong Kong Hong Kong   China
Hong Kong, Singapore, China Hong Kong   China
Honolulu, HI Honolulu Hawaii United States
Hoppers Crossing Hoppers Crossing Victoria Australia
Hot Israeli Behind an Uzi     Israel
Hotel California   California United States
Houma, La Houma Lousiana United States
Houston, Texas Houston Texas United States
Houston, Tx Houston Texas United States
Howard, WI Howard Wisconsin United States
http://us.newgoodz.com/twitter     Unknown
https://www.facebook.com/profile.php?id=100001265655241     Unknown
http://www.KatSwank.com     United States
http://www.sportspunter.com     Unknown
Hunter Valley, NSW Australia Hunter Valley New South Wales Australia
Huntington Beach Ca Huntington Beach California United States
Huntington Beach, CA Huntington Beach California United States
Hyderabad ,India Hyderabad   India
i e& MELBOURNE. Melbourne Victoria Australia
i heart melbourne Melbourne Victoria Australia
I Love San Diego San Diego California United States
I might be anywhere     Unknown
I still call Australia home!     Australia
I support Arizona   Arizona United States
Idaho, USA   Idaho United States
IL-CA   Illinois United States
Illawarra, Highlands – Throsby Illawarra New South Wales Australia
Illinois   Illinois United States
Im everywhere     Unknown
I’m EVERYWHERE, U NEVER THERE!     Unknown
I’m everywhere…     Unknown
I’m right here!     Unknown
Immune system     Unknown
in a hamster cage in san diego San Diego California United States
in a tree     Unknown
In Anthonys Heart!     Unknown
In closet wearing tinfoil hat.     United States
In front of a computer     Unknown
In hiding     Unknown
In my happy place, Australia     Australia
In my skin     Unknown
In The Kitchen     Australia
In the place they call OZ     Australia
in the pub     Unknown
in ur bed. zleeping     Unknown
in wonderland     Unknown
In your dreams baby     Australia
In your socks draw     Unknown
In your Xbox 360     Unknown
Independence, Missouri…USA Independence Missouri United States
india     India
India     India
INDIANA   Indiana United States
Indiana, USA   Indiana United States
Indianapolis Indianapolis Indiana United States
Indianapolis, IN Indianapolis Indiana United States
Indianapolis, Indiana Indianapolis Indiana United States
Indianapolis,USA Indianapolis Indiana United States
Indooroopilly, Brisbane Indooroopilly Queensland Australia
Indooroopilly, Queensland AU Indooroopilly Queensland Australia
Indore, Madhya Pradesh, India Indore Madhya Pradesh India
Inner West, Sydney Burwood New South Wales Australia
inner-west Sydney, AU Burwood New South Wales Australia
Insane Planet Earth     Unknown
Invisible airwaves     Unknown
Iowa   Iowa United States
IOWA The Tall Corn State   Iowa United States
Iowa, USA   Iowa United States
iPhone: 0.000000,0.000000     Unknown
iPhone: -25.353733,31.990084   Mpumalanga South Africa
iPhone: -26.627243,152.967651 Nambour Queensland Australia
iPhone: -27.324824,152.796478 Cedar Creek Queensland Australia
iPhone: -27.354387,153.071411 Boondall Queensland Australia
iPhone: -27.463465,153.011566 Brisbane Queensland Australia
iPhone: -27.463514,153.031494 Brisbane Queensland Australia
iPhone: -27.466549,153.046753 New Farm Queensland Australia
iPhone: -27.469229,153.025798 Brisbane Queensland Australia
iPhone: -27.469229,153.025798 Brisbane Queensland Australia
iPhone: -27.469482,152.987442 Bardon Queensland Australia
iPhone: -27.469828,153.026138 Brisbane Queensland Australia
iPhone: -27.661203,153.183487 Shailer Park Queensland Australia
iPhone: -31.9560013,115.860121 Perth Western Australia Australia
iPhone: -32.303307,115.756905 Cooloongup Western Australia Australia
iPhone: -32.336380,115.800153 Baldivis Western Australia Australia
iPhone: -32.747120,151.331421 Greta New South Wales Australia
iPhone: -32.910404,151.757721 Maryville New South Wales Australia
iPhone: 32.925980,-97.055859 Grapevine Texas United States
iPhone: 33.436928,-111.944595 Tempe Arizona United States
iPhone: 33.504520,-111.969727 Phoenix Arizona United States
iPhone: 33.655836,-117.747385 Irvine California United States
iPhone: -33.679794,150.926056 Rouse Hill New South Wales Australia
iPhone: -33.781571,151.049448 Carlingford New South Wales Australia
iPhone: -33.798124,151.277020 Fairlight New South Wales Australia
iPhone: -33.815075,151.198490 Naremburn New South Wales Australia
iPhone: -33.823381,151.197050 St Leonards New South Wales Australia
iPhone: -33.824276,151.199646 Crows Nest New South Wales Australia
iPhone: -33.868637,151.189728 Pyrmont New South Wales Australia
iPhone: -33.871689,151.207932 Sydney New South Wales Australia
iPhone: -33.874207,151.163986 Lilyfield New South Wales Australia
iPhone: -33.883141,151.176666 Camperdown New South Wales Australia
iPhone: -33.889645,151.278946 Bondi Beach New South Wales Australia
iPhone: -33.891506,151.212906 Surry Hills New South Wales Australia
iPhone: -33.911133,151.207382 Rosebery New South Wales Australia
iPhone: -33.911246,151.150609 Marrickville New South Wales Australia
iPhone: -33.911392,151.206798 Rosebery New South Wales Australia
iPhone: -33.917858,151.157104 Marrickville New South Wales Australia
iPhone: 33.940807,-118.406853 Los Angeles California United States
iPhone: 33.955437,-118.396156 Los Angeles California United States
iPhone: -33.970015,151.100334 Hurstville New South Wales Australia
iPhone: -34.031483,151.112915 Miranda New South Wales Australia
iPhone: 34.181973,-118.311799 Burbank California United States
iPhone: 34.200497,-118.348869 Burbank California United States
iPhone: -34.379795,150.902206 Towradgi New South Wales Australia
iPhone: -34.590698,138.912003 Altona South Australia Australia
iPhone: -34.921284,138.635239 Norwood South Australia Australia
iPhone: 36.972572,-122.036209 Santa Cruz California United States
iPhone: -37.388096,144.325025 Trentham Victoria Australia
iPhone: 37.4849S,144.5747Eÿþ Gisborne Victoria Australia
iPhone: -37.654083,144.426849 Darley Victoria Australia
iPhone: -37.682004,144.869102 Melbourne Airport Victoria Australia
iPhone: -37.703790,144.762172 Sydenham Victoria Australia
iPhone: -37.703790,144.762172 Sydenham Victoria Australia
iPhone: -37.726921,144.930679 Pascoe Vale Victoria Australia
iPhone: -37.736496,142.016205 Hamilton Victoria Australia
iPhone: -37.741932,144.898865 Essendon Victoria Australia
iPhone: -37.745468,144.732406 Caroline Springs Victoria Australia
iPhone: -37.759274,144.964600 Brunswick Victoria Australia
iPhone: 37.781475,-122.398758 San Francisco California United States
iPhone: -37.783516,144.983887 Fitzroy North Victoria Australia
iPhone: -37.787132,144.996323 Clifton Hill Victoria Australia
iPhone: -37.794126,144.947939 North Melbourne Victoria Australia
iPhone: -37.802803,144.904008 Footscray Victoria Australia
iPhone: -37.803372,144.996914 Abbotsford Victoria Australia
iPhone: -37.806587,144.962097 Melbourne Victoria Australia
iPhone: -37.813023,144.962097 Melbourne Victoria Australia
iPhone: -37.815231,144.965897 Melbourne Victoria Australia
iPhone: -37.817764,144.964554 Melbourne Victoria Australia
iPhone: -37.818790,145.003830 Richmond Victoria Australia
iPhone: -37.819820,144.967606 Southbank Victoria Australia
iPhone: -37.820038,144.985107   Victoria Australia
iPhone: -37.846709,144.999125 South Yarra Victoria Australia
iPhone: -37.921078,145.055817 Bentleigh East Victoria Australia
iPhone: -37.931778,145.055634 Bentleigh East Victoria Australia
iPhone: -37.950798,145.192764 Noble Park North Victoria Australia
iPhone: -38.022030,144.394196 Lara Victoria Australia
iPhone: 38.070183,-84.472343 Lexington Kentucky United States
iPhone: -38.071243,145.475485 Pakenham Victoria Australia
iPhone: -38.116077,144.352768 North Geelong Victoria Australia
iPhone: -38.146130,145.198624 Langwarrin Victoria Australia
iPhone: -38.146130,145.198624 Langwarrin Victoria Australia
iPhone: -38.178413,144.336517 Belmont Victoria Australia
iPhone: -38.197670,144.307556 Highton Victoria Australia
iPhone: -38.269573,144.647369 Queenscliff Victoria Australia
iPhone: 38.901371,-76.991074 Washington DC United States
iPhone: 39.016842,-77.034679 Silver Spring Maryland United States
iPhone: 39.661364,-75.561055 New Castle Delaware United States
iPhone: 39.999969,-75.237862 Lower Merion Pennsylvania United States
iPhone: 40.310623,-74.019081 Oceanport New Jersey United States
iPhone: 40.725206,-73.556488 East Meadow New York United States
iPhone: 42.091343,-71.264542 Foxborough Massachusetts United States
iPhone: 42.375122,-71.231750 Waltham Massachusetts United States
iPhone: 45.065475,-79.135880 Bracebridge Ontario Canada
iPhone: 45.812214,1.285503 Limoges Limousin France
iPhone: 49.266579,-123.102936 Vancouver British Columbia Canada
iPhone: 51.484146,-0.302932   Middlesex United Kingdom
iPhone: 52.349213,4.875060   North Holland Netherlands
iPhone: 52.364138,4.858794   North Holland Netherlands
iPhone: 53.343148,-6.258965 Dublin Dublin City Ireland
iPhone: -6.116976,106.856537   Jakarta Capital Region Indonesia
iPhone: Melbourne Melbourne Victoria Australia
iphone:35.273536,138.463586     Japan
iphone:37.489688,126.717461     South Korea
iphone:-37.82458333,144.961805 Southbank Victoria Australia
iphone:6.333059,217.926816     Unknown
Ipswich, QLD Australia Ipswich Queensland Australia
Ireland     Ireland
Irvine & Los Angeles, CA Irvine California United States
Irvine California Irvine California United States
Irvine, Ca Irvine California United States
Irvine, California Irvine California United States
Irvine, Scotland Irvine Scotland United Kingdom
Israel     Israel
Israel/USA     Israel
Jakarta, Melbourne Melbourne Victoria Australia
Jalan Sankis Depok, ID Depok   Indonesia
Jerusalem Jerusalem   Israel
Jerusalem, Israel Jerusalem   Israel
Jewelry Box     Unknown
John Wayne Gacy’s basement     United States
Johnstone Park, Geelong Geelong Victoria Australia
Joondalup, WA, Australia Joondalup Western Australia Australia
Jumping All Over the World     Unknown
June 1 Weeknights 11:35/10:35c New York City New York United States
Jurien Bay, WA. Jurien Bay Western Australia Australia
Just Brisbane. Brisbane Queensland Australia
just north of Sydney Blacktown New South Wales Australia
Kadıköy, Istanbul Istanbul   Turkey
Kagawa, Japan Kagawa   Japan
Kalgoorlie BOULDER Kalgoorlie Western Australia Australia
Kallangur, Brisbane, Australia Kallangur Queensland Australia
Kanga Kelpie Stud – Australia     Australia
Kangaroo Island, Australia Kingscote South Australia Australia
Kansas City Kansas City Kansas United States
Kansas City Missouri Kansas City Missouri United States
Kansas, USA   Kansas United States
Kapooka, NSW, Australia Kapooka New South Wales Australia
Katonah, NY Katonah New York United States
Katoomba Katoomba New South Wales Australia
Katoomba, New South Wales, Aus Katoomba New South Wales Australia
Katy, Texas Katy Texas United States
Kawa, Sydney Kawa New South Wales Australia
Kawaihae, Hawaii Kawaihae Hawaii United States
Kazakhstan, Almaty Almaty   Kazakhstan
Kellyville Ridge, NSW Kellyville Ridge New South Wales Australia
Kellyville, Sydney Australia Kellyville New South Wales Australia
Kenmore, NY Kenmore New York United States
Kent (Seattle), WA Kent Washington United States
Kingman, Arizona Kingman Arizona United States
Kingston, Tasmania Kingston Tasmania Australia
Knoxville, Tennessee (USA) Knoxville Tennessee United States
Koblenz Koblenz   Germany
Koenji Tokyo Japan Koenji Tokyo Japan
Kolkata,India Kolkata   India
Kolombo 03     Unknown
Kristiansand, Norway Kristiansand   Norway
Kuala Lumpur, Malaysia Kuala Lumpur   Malaysia
Kuranda, Cairns Kuranda Queensland Australia
Ku-ring-gai Ku-ring-gai New South Wales Australia
Kyneton, Victoria, Australia Kyneton Victoria Australia
L.A. / SYD / NZ Sydney New South Wales Australia
L.A., O.C., I.E., Ventura Los Angeles California United States
LA but eyes everywhere! Los Angeles California United States
LA CA Los Angeles California United States
La Follette, TN La Follette Tennesse United States
La Jolla CA La Jolla California United States
La Jolla, California La Jolla California United States
LA LA LAND     Unknown
la la land e& (Australia)     Australia
La Push, WA La Push Washington United States
La Rochelle / Paris Paris   France
LA, CA Los Angeles California United States
Labrador Labrador Newfoundland Canada
Ladder of Opportunity     Unknown
Lafayette, IN Lafayette Indiana United States
Laguna Niguel, California Laguna Niguel California United States
Lake District Kendal England United Kingdom
Lake Hopatcong, NJ USA Lake Hopatcong New Jersey United States
LakeClaremont,West Australia Lake Claremont Western Australia Australia
Lancashire, England Lancashire England United Kingdom
Lancaster, Lake District, UK Lancaster England United Kingdom
Land of Many Bananas     Unknown
Lane Cove North, Australia Lane Cove North New South Wales Australia
Las Vegas Las Vegas Nevada United States
Las Vegas Nv Las Vegas Nevada United States
Las Vegas, Chicago, Dallas Chicago Illinois United States
Las Vegas, Nevada Las Vegas Nevada United States
Las Vegas, Nevada NV Las Vegas Nevada United States
Las Vegas, NV Las Vegas Nevada United States
Las Vegas, NV 89143 Las Vegas Nevada United States
Las Vegas, NV, USA Las Vegas Nevada United States
Launceston Launceston Tasmania Australia
Launceston, Australia Launceston Tasmania Australia
Launceston, TAS, Australia Launceston Tasmania Australia
Launceston, Tasmania Launceston Tasmania Australia
Laying on the couch, Melbourne Melbourne Victoria Australia
Leeds Leeds England United Kingdom
Leeds, UK Leeds England United Kingdom
LETS HUNT SOME ORC!     Unknown
Lexington, Kentucky Lexington Kentucky United States
Liberty, MO Liberty Missouri United States
Limassol, Cyprus, CY Limassol   Cyprus
Linen House Oval, Moorabbin Moorabbin Victoria Australia
lisarow nsw Lisarow New South Wales Australia
Lisbon Lisbon   Portugal
Lithgow, NSW, Australia Lithgow New South Wales Australia
Living in Syd, 100% Melb boy Sydney New South Wales Australia
London London England United Kingdom
London (formally New York) London England United Kingdom
London (ish) London England United Kingdom
London , UK London England United Kingdom
London and Essex London England United Kingdom
London NW1 2DB London England United Kingdom
London UK London England United Kingdom
London, Boston, San Francisco London England United Kingdom
London, England London England United Kingdom
London, New York, Paris, Milan London England United Kingdom
London, Paris, New York London England United Kingdom
London, UK London England United Kingdom
london, vancouver, paris London England United Kingdom
London,UK London England United Kingdom
london/east coast London England United Kingdom
Londonish, England London England United Kingdom
Long Island NY Long Island New York United States
Longstanton, Cambridgeshire Longstanton England United Kingdom
Lorne, Great Ocean Road Lorne Victoria Australia
Los Angeles Los Angeles California United States
Los Angeles – USSA Los Angeles California United States
Los Angeles (via Boston) Los Angeles California United States
Los Angeles / Adelaide Adelaide South Australia Australia
Los Angeles / Nashville Los Angeles California United States
Los Angeles CA Los Angeles California United States
Los Angeles California Los Angeles California United States
Los Angeles County, CA, USA Los Angeles California United States
Los Angeles via Cleveland Los Angeles California United States
Los Angeles, CA Los Angeles California United States
LOS ANGELES, CA / WICHITA, KS Wichita Kansas United States
Los Angeles, CA | Central, NJ Los Angeles California United States
Los Angeles, CA, USA Los Angeles California United States
Los Angeles, CA, USA, Earth Los Angeles California United States
Los Angeles, Ca. Los Angeles California United States
Los Angeles, California Los Angeles California United States
Los Angeles, United States Los Angeles California United States
Los Angeles/NY/Miami Los Angeles California United States
Los Angeles-ish Los Angeles California United States
Los Angles, CA Los Angeles California United States
LOST     United States
Lost and I like it!     United States
Lost Angeles and New York Los Angeles California United States
Lost Angels Los Angeles California United States
Lost in a thought.     United States
lost in my head.     United States
Loughborough and Sydney Sydney New South Wales Australia
Louisville, KY Louisville Kentucky United States
Lubbock, TX Lubbock Texas United States
Lurking Behind Play     Unknown
Lurking…in Newcastle, NSW Newcastle New South Wales Australia
Luthercorp     Unknown
Lynbrook, New York Lynbrook New York United States
M Melbourne Victoria Australia
MA, USA     United States
Mableton, Georgia Mableton Georgia United States
Mackay Queensland Australia Mackay Queensland Australia
Mackay, QLD Mackay Queensland Australia
Macleod, Melbourne, Australia Macleod Victoria Australia
Macleod, Melbourne, Australia Melbourne Victoria Australia
Madison, WI Madison Wisconsin United States
Magic: 51.5957, -0.0560 Tottenham Greater London United Kingdom
Maharashtra, India Maharashtra   India
Main Street, USA     United States
Malibu, CA Malibu California United States
Malibu, California Malibu California United States
Mallala, SA Mallala South Australia Australia
Manatee County, Florida, USA   Florida United States
Manchester Manchester New Hampshire United States
Manchester / Weatherfield Manchester New Hampshire United States
Manchester, NH Manchester New Hampshire United States
Manchester, UK Manchester England United Kingdom
Manhattan Manhattan New York United States
manhattan! Manhattan New York United States
Manhattan, KS Manhattan Kansas United States
Manhattan, New York City Manhattan New York United States
Manhattan, New York City, USA Manhattan New York United States
Manipal, India Manipal   India
Manly/Darlinghurst, Sydney Manly New South Wales Australia
Manning, Western Australia AU Manning Western Australia Australia
Mansfield, VIC, Australia Mansfield Victoria Australia
Marianna, FL Marianna Florida United States
Marin County, CA Marin County California United States
Marin, California Marin County California United States
Maroochydore, QLD, Australia Maroochydore Queensland Australia
Marrickville, Australia Marrickville New South Wales Australia
Marrickville, NSW, Australia Marrickville New South Wales Australia
Marvellous Melbs Melbourne Victoria Australia
MARYLAND Melbourne Victoria Australia
Maryland   Maryland United States
Maryland, USA     United States
Massachusetts.     United States
Matraville, New South Wales AU Matraville New South Wales Australia
Mayo Mount Barker South Australia Australia
MCG Melbourne Victoria Australia
MCG, Melbourne, Australia Melbourne Victoria Australia
MD, USA     United States
Media, PA, USA     United States
Melb Melbourne Victoria Australia
MELB, AUSTRALIA. Melbourne Victoria Australia
Melb,Australia Melbourne Victoria Australia
melb.au Melbourne Victoria Australia
Melborne, Australia Melbourne Victoria Australia
Melboune Melbourne Victoria Australia
melboure Melbourne Victoria Australia
Melbourme, Australia Melbourne Victoria Australia
Melbourne Melbourne Victoria Australia
Melbourne Melbourne Victoria Australia
Melbourne & Geelong Geelong Victoria Australia
Melbourne & San Francisco Melbourne Victoria Australia
Melbourne (Bayside) Bayside Victoria Australia
Melbourne (usually!) Melbourne Victoria Australia
Melbourne / London Melbourne Victoria Australia
Melbourne :) , Australia Melbourne Victoria Australia
Melbourne and elsewhere Melbourne Victoria Australia
Melbourne and iPhone Melbourne Victoria Australia
Melbourne AU Melbourne Victoria Australia
melbourne au & nyc Melbourne Victoria Australia
Melbourne AUS Melbourne Victoria Australia
Melbourne Australia Melbourne Victoria Australia
Melbourne Australia Melbourne Victoria Australia
Melbourne Australia. Melbourne Victoria Australia
Melbourne baby.. Melbourne Melbourne Victoria Australia
Melbourne Beach, FL Melbourne Beach Florida United States
Melbourne City Melbourne Victoria Australia
Melbourne City, VIC Australia Melbourne Victoria Australia
MelBourne Identity Melbourne Victoria Australia
Melbourne northside Melbourne Victoria Australia
Melbourne OZ Melbourne Victoria Australia
Melbourne Town Melbourne Victoria Australia
Melbourne Town. Melbourne Victoria Australia
Melbourne Vi Australia Melbourne Victoria Australia
Melbourne VIC Melbourne Victoria Australia
Melbourne VIC AU Melbourne Victoria Australia
Melbourne VIC, Australia Melbourne Victoria Australia
Melbourne Victoria Australia Melbourne Victoria Australia
Melbourne! Melbourne Victoria Australia
Melbourne! The best city! Melbourne Victoria Australia
melbourne, Melbourne Victoria Australia
Melbourne, Australia Melbourne Victoria Australia
Melbourne, Asutralia Melbourne Victoria Australia
Melbourne, AU Melbourne Victoria Australia
Melbourne, Aus Melbourne Victoria Australia
Melbourne, Aust Melbourne Victoria Australia
Melbourne, Australia Melbourne Victoria Australia
Melbourne, Australia Melbourne Victoria Australia
Melbourne, Australia & Melbourne Victoria Australia
MELBOURNE, AUSTRALIA! Melbourne Victoria Australia
Melbourne, Australia!!! Melbourne Victoria Australia
Melbourne, Australia, 1985 Melbourne Victoria Australia
Melbourne, Australia. Melbourne Victoria Australia
Melbourne, Australia♥ Melbourne Victoria Australia
melbourne, earth Melbourne Victoria Australia
Melbourne, England uk :) . Melbourne England United Kingdom
melbourne, f’yeah! Melbourne Victoria Australia
Melbourne, MCG (Home Ground) Melbourne Victoria Australia
Melbourne, Oakland, London Melbourne Victoria Australia
Melbourne, Oz Melbourne Victoria Australia
Melbourne, sometimes Melbourne Victoria Australia
Melbourne, Sydney & Brisbane Sydney New South Wales Australia
Melbourne, Sydney, Australia Melbourne Victoria Australia
Melbourne, VIC Melbourne Victoria Australia
Melbourne, Vic Australia Melbourne Victoria Australia
Melbourne, VIC, AU Melbourne Victoria Australia
Melbourne, Vic, AUS Melbourne Victoria Australia
Melbourne, VIC, Australia Melbourne Victoria Australia
Melbourne, Vic. Melbourne Victoria Australia
Melbourne, VIC. Australia Melbourne Victoria Australia
Melbourne, Victoria Melbourne Victoria Australia
Melbourne, Victoria Melbourne Victoria Australia
Melbourne, Victoria, AU Melbourne Victoria Australia
Melbourne, Victoria, Australai Melbourne Victoria Australia
Melbourne, Victoria, Australia Melbourne Victoria Australia
melbourne,australia Melbourne Victoria Australia
Melbourne,VIC Melbourne Victoria Australia
Melbourne,VIC, Australia Melbourne Victoria Australia
melbourne. Melbourne Victoria Australia
Melbourne. Australia Melbourne Victoria Australia
Melbourne. My hometown. Melbourne Victoria Australia
Melbourne….but not for long Melbourne Victoria Australia
Melbourne/Geelong Australia Geelong Victoria Australia
Melbourne/Hobart Hobart Tasmania Australia
Melbourne/Sydney, Australia Melbourne Victoria Australia
melbourneee   Victoria Australia
Melbournetown Melbourne Victoria Australia
Melbourne-town Melbourne Victoria Australia
melbourneVICTORIAau Melbourne Victoria Australia
Melbs Melbourne Victoria Australia
melbs, australia Melbourne Victoria Australia
Melburn Melbourne Victoria Australia
Melbyn, Australia Melbourne Victoria Australia
Mell-born, Australia Melbourne Victoria Australia
Mesa, Arizona Mesa Arizona United States
Mesquite, TX Mesquite Texas United States
Mexico     Mexico
Mexico City Mexico City   Mexico
Mexico City, Mexico Mexico City   Mexico
MI   Michigan United States
MI USA   Michigan United States
Miami Miami Florida United States
Miami Beach, FL /Sydney, Austr Sydney New South Wales Australia
MIami Florida USA Miami Florida United States
Miami USA Miami Florida United States
Miami, FL, USA Miami Florida United States
Miami, Fl. USA Miami Florida United States
MiChiGaN   Michigan United States
Michigan, USA   Michigan United States
Mid North Coast, NSW Coffs Harbour New South Wales Australia
Middle of Nowhere, USA     United States
Middle of Texas   Texas United States
Middle Swan WA Middle Swan Western Australia Australia
Middlesbrough / England Middlesbrough England United Kingdom
Midland, TX Midland Texas United States
Midwest, USA     United States
Mildura, Australia Mildura Victoria Australia
Millicent, Australia Millicent South Australia Australia
Milwaukee, WI Milwaukee Wisconsin United States
Minneapolis Minneapolis Minnesotta United States
Minneapolis Airport Restroom Minneapolis Minnesotta United States
Minneapolis Minnesota USA Minneapolis Minnesotta United States
Minneapolis, MN Minneapolis Minnesotta United States
Minnesota USA   Minnesotta United States
Minnesota, USA   Minnesotta United States
Missouri/ Washington DC   Missouri United States
Mitcham Mitcham Victoria Australia
mitt, nsw, Australia Mitt New South Wales Australia
Mn 6th district, USA     United States
Moe, VIC, Australia Moe Victoria Australia
Mollymook, Australia Mollymook New South Wales Australia
Monterey, CA Monterey California United States
Monterey, California Monterey California United States
Monterey, California USA Monterey California United States
Montreal Montreal Quebec Canada
Montréal, Canada Montreal Quebec Canada
Montreal, Quebec, Canada Montreal Quebec Canada
Montrose. Montrose Tasmania Australia
Mooers NY USA Mooers New York United States
Mooloolaba, Qld, Australia Mooloolaba Queensland Australia
Moonee Ponds, Victoria AU Moonee Ponds Victoria Australia
Moreno Valley, California Moreno Valley California United States
Mornington Peninsula. Mornington Peninsula Victoria Australia
Mornington, Melbourne, Vic Mornington Victoria Australia
Morwell, vic Morwell Victoria Australia
Moscow, Russia Moscow   Russia
Mostly Los Angeles Los Angeles California United States
mostly Sydney Sydney New South Wales Australia
Mount Druitt, NSW Mount Druitt New South Wales Australia
Mount Isa QLD Mount Isa Queensland Australia
Mount Vernon, WA USA Mount Vernon Washington United States
Mountain View, CA Mountain View California United States
Mountain View, California Mountain View California United States
Mt. Isa, Australia Mt. Isa Queensland Australia
Mumbai, India Mumbai   India
Murrumbateman NSW, AU Murrumbateman New South Wales Australia
My heart beats for Melbourne Melbourne Victoria Australia
My Heart Belongs to NYC New York City New York United States
My home city is Sydney Sydney New South Wales Australia
My Own Little World (Apex, NC) Apex North Carolina United States
My washing machine lives inABQ   New Mexico United States
my world in Cali.   California United States
N44º 31.308 W122º 55.223 Lebanon Oregon United States
N48° 1.1692′, W122° 4.8227′ Lake Stevens Washington United States
Nanjing, China Nanjing   China
Narrandera, NSW, Australia Narrandera New South Wales Australia
Nashvegas Nashville Tennessee United States
Nashville, TN, USA Nashville Tennessee United States
Nashville/Los Angeles Nashville Tennessee United States
Nation Wide     Unknown
Natural Born~USA     United States
NC, USA   North Carolina United States
NC-7 North Carolina   North Carolina United States
Near #Sydney, #AUSTRALIA Sydney New South Wales Australia
Near a beach, in Australia     Australia
Near Denver Colorado Denver Colorado United States
Near the beach, Vic, Australia Bells Beach Victoria Australia
Nebraska, USA   Nebraska United States
Needles, California, USA Needles California United States
neverland 3     United States
Neverland, Australia.     Australia
NEW ALBUM 9.14     Unknown
new Berlin, Germany Berlin   Germany
new delhi, india New Delhi   India
New England New England New South Wales Australia
New Jersey, USA   New Jersey United States
New Mexico   New Mexico Mexico
New Norfolk, Tasmania New Norfolk Tasmania Australia
New Orleans, LA New Orleans Louisiana United States
New Orleans, Miami, ATL Miami Florida United States
New South Wales   New South Wales Australia
New South Wales, Australia   New South Wales Australia
New South Wales, Clovelly Clovelly New South Wales Australia
New South Wales, Newtown Newtown New South Wales Australia
New South Wales, Sydney Sydney New South Wales Australia
new tokyo Tokyo   Japan
New Yawkkk   New York United States
New York New York City New York United States
New york New York City New York United States
New York – Augusta Augusta New York United States
New York & Geneva, Switzerland New York City New York United States
New York / Washington DC New York City New York United States
New York and beyond… New York City New York United States
New York and planet Xclusive New York City New York United States
New York City New York City New York United States
New York City & San Francisco New York City New York United States
New York City / San Francisco New York City New York United States
New York City and Texas New York City New York United States
New York City, Baby! New York City New York United States
New York City, New York New York City New York United States
New York City, NY New York City New York United States
New York City, NY, USA New York City New York United States
New York NY New York City New York United States
New York NY (Forest Hills) USA Forst Hills New York United States
New York or Florida New York City New York United States
New York State   New York United States
New York, London, Paris, Tokyo New York City New York United States
New York, N.Y. New York City New York United States
New York, New Orleans, Paris Paris   France
New York, New York New York City New York United States
New York, NY New York City New York United States
New York, NY (sometimes) New York City New York United States
New York, NY 10019 New York City New York United States
new York, NY USA New York City New York United States
New York, NY, USA New York City New York United States
New York, USA New York City New York United States
New Zealand     New Zealand
New Zealand, Illinois.   Illinois United States
Newark Newark New Jersey United States
Newark, NJ Newark New Jersey United States
Newborough, Victoria Newborough Victoria Australia
Newcastle Newcastle New South Wales Australia
Newcastle Australia Newcastle New South Wales Australia
Newcastle NSW Australia Newcastle New South Wales Australia
Newcastle NSW, Australia Newcastle New South Wales Australia
Newcastle, Australia Newcastle New South Wales Australia
Newcastle, New South Wales, Au Newcastle New South Wales Australia
Newcastle, NSW Newcastle New South Wales Australia
Newcastle, NSW, Australia Newcastle New South Wales Australia
Newcastle, UK Newcastle England United Kingdom
Newcastle, UK. Newcastle England United Kingdom
Newcastle/ Lake Macquarie Newcastle New South Wales Australia
NEWCASTLE/SYDNEY (more Sydney) Sydney New South Wales Australia
Newtown via Moree Newtown New South Wales Australia
Newtown, Sydney, Australia Newtown New South Wales Australia
Newtown, Sydney, NSW Newtown New South Wales Australia
NJ USA   New Jersey United States
NOGO (North of Chicago) Evanston Illinois United States
Nomadic. Now: London London England United Kingdom
Noosa Australia Noosa Queensland Australia
Noosa, Australia Noosa New South Wales Australia
Noosa, QLD + Eastern Subs, VIC Noosa Queensland Australia
Norfolk, VA/ Los Angeles, CA Norfolk Virginia United States
North America     United States
North Carlton North Carlton Victoria Australia
North Carolina   North Carolina United States
North Carolina, USA   North Carolina United States
North Carolina, USSA   North Carolina United States
North Coast NSW Coffs Harbour New South Wales Australia
North East USA     United States
North Melbourne North Melbourne Victoria Australia
North of San Francisco Oakland California United States
North Queensland Townsville Queensland Australia
North Ryde, NSW, Australia North Ryde New South Wales Australia
North Sydney North Sydney New South Wales Australia
North Texas   Texas United States
North Texas, USA   Texas United States
Northcote VIC, Australia Northcote Victoria Australia
Northcote, Melbourne Northcote Victoria Australia
Northcote, Victoria Northcote Victoria Australia
Northern Beaches, Sydney Port Jackson New South Wales Australia
northern CA Sacramento California United States
Northmead, NSW, Australia Northmead New South Wales Australia
Northridge, CA Northridge California United States
northside, melbourne North Melbourne Victoria Australia
Northwest Indiana   Indiana United States
North-west Staffordshire, UK Staffordshire England United Kingdom
Northwest Sydney Blacktown New South Wales Australia
Northwest Sydney Sydney New South Wales Australia
Not Collins St, Melbourne Melbourne Victoria Australia
Not Guam Hagåtña   Guam
not there     Unknown
Noth Dakota, USA   North Dakota United States
Notting Hill, London, UK Notting Hill England United Kingdom
Nottingham Nottingham England United Kingdom
Nowhere and Everywhere     Unknown
Nowhere for too long…     Unknown
Nowra Nowra New South Wales Australia
NSW   New South Wales Australia
NSW Australia   New South Wales Australia
NSW Central Coast Gosford New South Wales Australia
NSW South Coast Shoalhaven New South Wales Australia
NSW, Aussie Babe!   New South Wales Australia
NSW, Australia   New South Wales Australia
NSW, Australia.   New South Wales Australia
NT, Australia   Northern Territory Australia
Nundah, Queensland AU Nundah Queensland Australia
NY   New York United States
NY – Where else?     Unknown
NY, NY New York City New York United States
NY/LA New York City New York United States
NYC New York City New York United States
NYC presently New York City New York United States
NYC & San Francisco New York City New York United States
NYC / ATL (on occasion) New York City New York United States
NYC / Miami New York City New York United States
NYC / SF New York City New York United States
NYC, NY New York City New York United States
NYC, NY 10003 New York City New York United States
NYC, worldwide New York City New York United States
NYC: 40.739069,-73.987082 New York City New York United States
NY-LA New York City New York United States
Oak Flats NSW Australia Oak Flats New South Wales Australia
Oak Park, Illinois Oak Park Illinois United States
Oakland Ca USA Oakland California United States
Oakland, CA Oakland California United States
Oakland, California Oakland California United States
Off Broome, Western Australia Derby Western Australia Australia
off highway 11 in nd   North Dakota United States
OH, NYC, CA   Ohio United States
Ohio,USA   Ohio United States
Ojai, California Ojai California United States
Oklahoma   Oklahoma United States
Old St Pancras, London London England United Kingdom
Olympia wa Olympia Washington United States
Olympia, WA Olympia Washington United States
Omaha Mall/ Aussie land!     Australia
On a pale blue dot     Unknown
On A plane, California   California United States
On a shining blue dot     Unknown
on here     Unknown
On my couch in Melbourne Melbourne Victoria Australia
on my phone. lots.     Unknown
On my way to the top…     Unknown
On stage in Huntington Beach Huntington Beach California United States
On the Beach, San Francisco San Francisco California United States
On The Coast of NC   North California United States
On The Coast Of Some Where     United States
On the Net     Unknown
On the road in Victoria   Victoria Australia
on the road with Tony Abbott Canberra Australian Capital Territory Australia
On top     Unknown
On Tour     Unknown
On your iPod     Unknown
Once-Great America     United States
Online     Unknown
Ontario, Canada   Ontario Canada
Orange County Orange County California United States
Orange County, CA Orange County California United States
Orange County, California, USA Orange County California United States
Orange, NSW. Orange New South Wales Australia
Oregon   Oregon United States
Orlando, FL Orlando Florida United States
Ormond, Vic Ormond Victoria Australia
Oshawa, Ontario Canada Oshawa Ontario Canada
Oslo, Norway Oslo   Norway
Oswego Ny Oswego New York United States
Ottawa Ottawa Ontario Canada
Ottawa, Canada Ottawa Ontario Canada
Ottawa, ON Ottawa Ontario Canada
Ottawa, ON, Canada Ottawa Ontario Canada
Ottawa, Ontario Ottawa Ontario Canada
Ottawa, Ontario, Canada Ottawa Ontario Canada
out there, aotearoa     New Zealand
Outdmix, TX Outdmix Texas United States
Outer east of Melbourne Boroondara Victoria Australia
Outer North, Melbourne Whittlesea Victoria Australia
Outside South Bend, Indiana South Bend Indiana United States
Oxford, UK Oxford England United Kingdom
oz     Australia
oz!!     Australia
Ozone Park, New York Ozone Park New York United States
PA   Pennsylvania United States
Pac NW Melbourne Melbourne Victoria Australia
Palatine, Illinois Palatine Illinois United States
Palm Desert, ca Palm Desert California United States
Palm Springs, CA Palms Spring California United States
Palo Alto, CA Palo Alto California United States
Palo Alto, California Palo Alto California United States
Paris Paris   France
Paris, Earth Paris   France
Paris, France Paris   France
Paris, France, Twitterverse Paris   France
Paris, Île-de-France Paris   France
Paris, Tx Paris Texas United States
Park St. brunswick MELBOURNE Brunswick Victoria Australia
Parkville Campus, Melbourne Parkville Victoria Australia
Parliament House, Canberra Canberra Australian Capital Territory Australia
Parts Unknown     Unknown
Pasadena, CA Pasadena California United States
Paterson Federal Electorate Paterson New South Wales Australia
Paturn, Western Australia Paturn Western Australia Australia
Pennsylvania USA   Pennsylvania United States
Pennsylvania, USA   Pennsylvania United States
perth Perth Western Australia Australia
Perth Western Australia Perth Western Australia Australia
Perth – Western Australia Perth Western Australia Australia
Perth , Australia Perth Western Australia Australia
Perth and Manila Perth Western Australia Australia
Perth Australia Perth Western Australia Australia
Perth of course! Perth Western Australia Australia
Perth WA Perth Western Australia Australia
Perth WA Australia Perth Western Australia Australia
Perth Western Australia Perth Western Australia Australia
Perth Western Australia! Perth Western Australia Australia
Perth, Aus Perth Western Australia Australia
Perth, Australia Perth Western Australia Australia
Perth, WA Perth Western Australia Australia
Perth, WA, Australia Perth Western Australia Australia
Perth, West Australia Perth Western Australia Australia
Perth, West Oz Perth Western Australia Australia
Perth, Western Austalia Perth Western Australia Australia
Perth, Western Australia Perth Western Australia Australia
Perth,Australia Perth Western Australia Australia
Perth,WA Perth Western Australia Australia
perth/hobart/amsterdam Hobart Tasmania Australia
philadelphia Philadelphia Pennsylvania United States
Philadelphia, PA Philadelphia Pennsylvania United States
Phoenix Phoenix Arizona United States
Phoenix, Arizona Phoenix Arizona United States
picnic pt Picnic Point New South Wales Australia
Pinckney, Michigan Pinckney Michigan United States
Pittsboro, IN Pittsboro Indiana United States
Pittsburgh, PA, USA Pittsburgh Pennsylvania United States
Pixie Hollow :D     Unknown
Plainview, NY Plainview New York United States
Planet Earth     Unknown
Planet Earth :)     Unknown
Planet Earth somewhere IN U     Unknown
Plano, Texas Plano Texas United States
Playa Del Carmen, Mexico Playa Del Carmen   Mexico
Pleasant Grove, UT Pleasant Grove Utah United States
Plymouth Plymouth England United Kingdom
Plymouth UK Plymouth England United Kingdom
Plymouth, Devon Plymouth England United Kingdom
Plymouth, Devon, UK. Plymouth England United Kingdom
Plymouth, UK Plymouth England United Kingdom
Point Cook, Victoria Point Cook Victoria Australia
Pointe-Claire, QC Pointe-Claire Quebec Canada
Port Arthur, TX Port Arthur Texas United States
Port Douglas Port Douglas Queensland Australia
Port Melbourne Port Melbourne Victoria Australia
Port St. Lucie, Florida Port St. Lucie Florida United States
Port Stephens Corlette New South Wales Australia
Port Stephens, Australia Corlette New South Wales Australia
Port Stephens, Australia Port Stephens New South Wales Australia
Portland Portland Oregon United States
Portland Oregon,Vancouver BC Portland Oregon United States
Portland, Maine Portland Maine United States
Portland, OR Portland Oregon United States
Portland, OR and the World Portland Oregon United States
Portland, OR, USA Portland Oregon United States
Portland, Oregon Portland Oregon United States
Portland, Oregon, USA Portland Oregon United States
Portland,OR Portland Oregon United States
Porto Alegre Porto Alegre   Brazil
Porto Alegre – RS Porto Alegre   Brazil
Portsea, Australia Portsea Victoria Australia
Portsmouth, Hampshire, UK Portsmouth England United Kingdom
Portsmouth, NH Portsmouth New Hampshire United States
portugal     Portugal
Prahran, AUS Prahran Victoria Australia
Prahran, Melbourne Prahran Victoria Australia
Prahran, VIC Prahran Victoria Australia
Premiership Dais   England United Kingdom
Preston, VIC, Australia Preston Victoria Australia
Probably Lost     United States
Puget Sound, Washington, USA Puget Sound Washington United States
Pune, India Pune   India
Punt Road Oval, Richmond Richmond Victoria Australia
Purchase, New York Purchase New York United States
Pyrmont, Sydney Pyrmont New South Wales Australia
QC, Canada   Quebec Canada
QLD   Queensland Australia
QLD Australia   Queensland Australia
Qld, Australia   Queensland Australia
QLD.   Queensland Australia
Q’town, NSW, Australia Queanbeyan New South Wales Australia
Quakers Hill NSW Australia Quakers Hill New South Wales Australia
Queensland   Queensland Australia
Queensland Australia   Queensland Australia
Queensland, Australia   Queensland Australia
Queensland, mostly Brisbane Brisbane Queensland Australia
Queenstown, New Zealand     New Zealand
Racoon City     Unknown
rAdelaide, Australia Adelaide South Australia Australia
Radelaide, SA Adelaide South Australia Australia
Ramara Tsp, ON Ramara Ontario Canada
Randwick, Sydney Randwick New South Wales Australia
Ranking something somewhere     Unknown
REAL AMERICA     United States
redcliffe qld Redcliffe Queensland Australia
Redding California Redding California United States
Redmond – But Everywhere Redmond Washington Unknown
Redmond, WA Redmond Washington United States
Redmond, WA, and NYC Redmond Washington United States
Redwood City, CA Redwood City California United States
Redwood City,CA: 37.49,-122.22 Redwood City California United States
Renton, WA Renton Washington United States
Republic of Texas   Texas United States
Reservoir, Victoria Reservoir Victoria Australia
Richmond B.C. Canada, Richmond British Columbia Canada
Richmond Melb. Richmond Victoria Australia
Richmond, Va. USA Richmond Virginia United States
Richmond, Victoria Richmond Victoria Australia
Richmond, Virginia Richmond Virginia United States
Richmond, Virginia, USA, earth Richmond Virginia United States
Ridley PA Ridley Pennsylvania United States
right here     Unknown
Right here \/     Unknown
Right here, on the right side.     Unknown
Right where God wants me     United States
Rill ‘murika city by the Bay     United States
Ringwood, Victoria, Australia Ringwood Victoria Australia
Rio de Janeiro – Brazil Rio de Janeiro   Brazil
Riverland South Australia Roverland South Australia Australia
Riverside, California Riverside California United States
Robina, Gold Coast Robina Queensland Australia
Rochester, NY Rochester New York United States
Rockaway, N.J. Rockaway New Jersey United States
Rockford, IL, USA Rockford Illinois United States
Rockhampton, Qld Rockhampton Queensland Australia
Rockin’ Bristol, CT since 1979 Bristol Connecticut United States
Rockingham, Winston-Salem, NC Rockingham North Carolina United States
ROLL TIDE, USA     United States
ROMA, QUEENSLAND Roma Queensland Australia
Roma, Queensland, Australia Roma Queensland Australia
rooted in melbourne Melbourne Victoria Australia
Ross, CA Ross California United States
Round Rock, TX Round Rock Texas United States
Royal Hertfordshire Hertfordshire England United Kingdom
Russia     Russia
s aust   South Australia Australia
S.E. Qld, Australia Toowoomba Queensland Australia
SA, Australia   South Australia Australia
Sacramento, CA Sacramento California United States
Sacramento, Santa Barbara, Ca Sacramento California United States
Sacramento,Ca Sacramento California United States
Saint John NB Canada Saint John New Brunswick Canada
Saint Paul, Minnesota St Paul Minnesota United States
Sale, Victoria, Australia Sale Victoria Australia
Salt Lake City Salt Lake City Utah United States
Salt Lake City, UT Salt Lake City Utah United States
Salzburg – Austria Salzburg   Austria
San Antonio San Antonio Texas United States
San Antonio Texas San Antonio Texas United States
San Antonio Tx San Antonio Texas United States
San Antonio, Texas San Antonio Texas United States
San Antonio, Texas area San Antonio Texas United States
San Antonio, TX San Antonio Texas United States
San Antonio, TX, USA San Antonio Texas United States
San Bruno, CA San Bruno California United States
San Clemente, CA, USA San Clemente California United States
San Diego San Diego California United States
San Diego and Area 51 San Diego California United States
San Diego and everywhere else San Diego California United States
San Diego, CA San Diego California United States
San Diego, California San Diego California United States
San Diego, California, USA San Diego California United States
San Francisco San Francicso California United States
San Francisco & Geneva San Francicso California United States
San Francisco (via Atlanta) San Francisco California United States
San Francisco and Truckee, CA Truckee California United States
San Francisco Bay Area San Francisco California United States
San Francisco, CA San Francicso California United States
San Francisco, CA 94103 San Francisco California United States
San Francisco, CA, US San Francisco California United States
San Francisco, CA, USA San Francisco California United States
San Francisco, California San Francisco California United States
San Francisco/New York San Francisco California United States
San Jose, CA San Jose California United States
San Jose,CA San Jose California United States
San Marcos, Texas, USA San Marcos Texas United States
San Mateo, CA San Mateo California United States
San Pedro Sula, Honduras San Pedro Sula   Honduras
San Rafael, California, USA San Rafael California United States
San Tan Valley, Arizona San Tan Valley Arizona United States
Sane Francisco San Francisco California United States
Santa Barbara, CA Santa Barbara California United States
Santa Cruz, CA Santa Cruz California United States
Santa Monica Santa Monica California United States
Santa Monica, CA Santa Monica California United States
Santa Monica, California Santa Monica California United States
Santa Rosa, Ca Santa Rosa California United States
Sao Paulo São Paulo   Brazil
São Paulo, BR São Paulo   Brazil
São Paulo, Brazil São Paulo   Brazil
Saratoga County, NY Saratoga County New York United States
Sassafras, Victoria AU Sassafras Victoria Australia
Sault Ste. Marie, Michigan USA Sault Ste. Marie Michigan United States
Savannah, GA Savannah Georgia United States
Savannah, Georgia Savannah Georgia United States
Scotland   Scotland United Kingdom
Scotland / SF   Scotland United Kingdom
Scotland/ Ayrshire Ayrshire Scotland United Kingdom
Scottsdale, Arizona Scottsdale Arizona United States
Scottsdale, Arizona USA Scottsdale Arizona United States
SE Australia     Australia
Seal Beach, CA Seal Beach California United States
Seattle Seattle Washington United States
Seattle USA Seattle Washington United States
Seattle WA Seattle Washington United States
Seattle, US Seattle Washington United States
Seattle, Utah, LA, NY, BC Seattle Washington United States
Seattle, WA Seattle Washington United States
Seattle, Washington Seattle Washington United States
Seattle/Soon to be Texas! Seattle Washington United States
Seattle/Vancouver/LA Seattle Washington United States
Seaview WA Seaview Washington United States
Sebastopol, CA Sebastopol California United States
Seddon, Victoria, Australia Seddon Victoria Australia
Sedney Australia Sydney New South Wales Australia
Sedona, Arizona Sedona Arizona United States
Serving Australia wide & Int’l     Australia
Seven Network     Australia
SF Bay Area – San Jose, CA San Jose California United States
SF Bay Area, California Palo Alto California United States
sf, ca San Francisco California United States
Shanghai, China Shanghai   China
Sharon, MA USA Sharon Massachusetts United States
Shawnee, KS Shawnee Kansas United States
Sheffield, UK     United Kingdom
Shellharbour, NSW, Australia Shellharbour New South Wales Australia
Shepparton Australia Shepparton Victoria Australia
Shepparton, Australia Shepparton Victoria Australia
Shepparton, Vic Shepparton Victoria Australia
Shreveport, Louisiana Shreveport Louisiana United States
Sidderknee Sydney New South Wales Australia
Sign for The 5 Mistakes Report     Australia
Silicon Valley, CA Palo Alto California United States
Silver Spring, MD, USA Silver Springs Maryland United States
Singapore Singapore   Singapore
Singapore and Sydney Sydney New South Wales Australia
sleeping in the ghettooooo     Unknown
Smellbourne South Melbourne Victoria Australia
SoCal/USA San Diego California United States
Socialist America, WA   Washington United States
Socialist Chicago, Illinois Chicago Illinois United States
some beach in Sydney Sydney New South Wales Australia
Some where in the south     United States
some where in the world     Unknown
Some Where USA     United States
sometimes in Sydney Sydney New South Wales Australia
Somewhere     Unknown
Somewhere in Hawaii   Hawaii United States
Somewhere in L.A blowing trees Los Angeles California United States
Somewhere in Melbourne Melbourne Victoria Australia
Somewhere in my head     Unknown
Somewhere not here     Unknown
somewhere on earth..!!     Unknown
Somewhere on the Gold Coast Gold Coast Queensland Australia
Somewhere out there in Libland     Unknown
Somewhere Over The Rainbow     Unknown
Sonora, California Sonora California United States
Soon: The Lodge Canberra Australian Capital Territory Australia
Sort of near Los Angeles USA Los Angeles California United States
South Australia   South Australia Australia
South Australia, Australia   South Australia Australia
South Bay of LA     United States
South Carolina   South Carolina United States
South Coast NSW Illawarra New South Wales Australia
South Coast NSW Australia Shoalhaven New South Wales Australia
South Coastal Massachusetts   Massachusetts United States
South Florida USA   Florida United States
South Jersey   New Jersey United States
South London South London England United Kingdom
South Melbourne South Melbourne Victoria Australia
South Melbourne, Australia South Melbourne Victoria Australia
South Melbourne, Victoria South Melbourne Victoria Australia
South of the border Australia.   South Australia Australia
South oz   South Australia Australia
South Perth, WA, Australia South Perth Western Australia Australia
SOUTH USA     United States
South West, Australia Harvey Western Australia Australia
South West, Western Australia. Bunbury Western Australia Australia
South Yarra, Melbourne South Yarra Victoria Australia
Southampton, NY Southampton New York United States
SouthAust.   South Australia Australia
Southbank, Melbourne Southbank Victoria Australia
Southboro MA, USA Earth Southboro Massachusetts United States
Southeast USA     United States
Southend-On-Sea, Essex, UK Southend-On-Sea England United Kingdom
Southern California   California United States
Southern Hemisphere     Australia
Southern Indiana   Indiana United States
Southern USA     United States
Southlake, TX Southlake Texas United States
Southwest Texas   Texas United States
Southwestern USA   New Mexico United States
SP/Brazil – Orlando/FL São Paulo   Brazil
Space Coast, FL, USA Space Coast Florida United States
Spence, ACT, Australia Spence Australian Capital Territory Australia
Spencer, IA Spencer Iowa United States
Spokane, Washington Spokane Washington United States
Sport SENtral Melbourne Victoria Australia
Sportingbet Australia   Victoria Australia
Sports Universe     United States
St George and the Illawarra Illawarra New South Wales Australia
St K, Melbourne, Australia St. Kilda Victoria Australia
St Kilda St Kilda Victoria Australia
St Kilda, Australia St Kilda Victoria Australia
St Kilda, Melbourne   Victoria Australia
St Kilda, VIC. St Kilda Victoria Australia
St Leonards, Victoria St Leonards Victoria Australia
St Louis St Louis Missouri United States
St Louis, MO, USA St Louis Missouri United States
St. Joseph, MO St. Joseph Missouri United States
St. Louis MO St. Louis Missouri United States
St.Kilda, VIC St Kilda Victoria Australia
stanmore nsw australia Stanmore New South Wales Australia
Staten Island, New York Staten Island New York United States
Stawell, Australia Stawell Victoria Australia
Still on the beach.     Australia
Stirling, South Australia Stirling South Australia Australia
Stoke Newington, London Stoke Newington England United Kingdom
Straight outta Melbourne Melbourne Victoria Australia
Strängnäs, Sweden Strängnäs   Sweden
Strathfield, NSW, Australia Strathfield New South Wales Australia
Studio 1A New York City New York United States
Subiaco, Perth, WA Subiaco Western Australia Australia
Subiaco, Western Australia Subiaco Western Australia Australia
Sugar Land, Texas Sugarland Texas United States
Sugarland, TX Sugarland Texas United States
Sunbury Australia Sunbury Victoria Australia
Sunny Canberra Canberra Australian Capital Territory Australia
Sunny Coast Sunshine Coast Queensland Australia
Sunny Communist China     China
Sunny Queensland   Queensland Australia
Sunny Queensland, Australia   Queensland Australia
Sunny So. California San Diego California United States
sunnycoast, qld Sunshine Coast Queensland Australia
Sunnyvale CA Sunnyvale California United States
Sunnyvale, CA Sunnyvale California United States
Sunshine Coast Sunshine Coast Queensland Australia
Sunshine Coast Sunshine Coast Queensland Australia
Sunshine Coast Australia Sunshine Coast Queensland Australia
Sunshine Coast, Aus. Sunshine Coast Queensland Australia
Sunshine Coast, Australia Sunshine Coast Queensland Australia
Sunshine Coast, Qld Sunshine Coast Queensland Australia
Sunshine Coast, QLD, Australia Sunshine Coast Queensland Australia
Sunshine Coast, Queensland Sunshine Coast Queensland Australia
Sunshine. Lollipops. Rainbows.     Unknown
Surabaya Surabaya   Indonesia
Surf Coast Jan Juc Australia Jan Juc Victoria Australia
surf sun and fun     Unknown
Surfers Paradise Gold Coast Surfers Paradise Queensland Australia
Surry Hills, Sydney Surry Hills New South Wales Australia
Surry Hills, Sydney, Australia Surry Hills New South Wales Australia
Sussex, England and Australia Sussex England United Kingdom
Suzhou, China Suzhou   China
Swindon, UK Swindon England United Kingdom
SWMI (my heart’s w/ Israel)     Israel
syd en eee Sydney New South Wales Australia
Syd, NSW Sydney New South Wales Australia
Syd-a-ney Sydney New South Wales Australia
Sydney Sydney New South Wales Australia
Sydney Sydney New South Wales Australia
Sydney Australia Sydney New South Wales Australia
Sydney – Australia Sydney New South Wales Australia
Sydney & Canberra Canberra Australian Capital Territory Australia
Sydney & Canberra, Australia Canberra Australian Capital Territory Australia
Sydney & London Sydney New South Wales Australia
Sydney (Inner City) Sydney New South Wales Australia
Sydney (interstate on fridays) Sydney New South Wales Australia
Sydney (Ryde), Australia Ryde New South Wales Australia
Sydney , Australia. Sydney New South Wales Australia
Sydney / Orange County Sydney New South Wales Australia
Sydney AUS & Los Angeles USA Sydney New South Wales Australia
Sydney Aust Sydney New South Wales Australia
Sydney Australia Sydney New South Wales Australia
Sydney- Australia Sydney New South Wales Australia
Sydney AUSTRALIA! Sydney New South Wales Australia
Sydney based Sydney New South Wales Australia
Sydney NSW Sydney New South Wales Australia
Sydney OZ Sydney New South Wales Australia
sydney yo Sydney New South Wales Australia
Sydney, Australia Sydney New South Wales Australia
Sydney, AU Sydney New South Wales Australia
Sydney, Aus Sydney New South Wales Australia
Sydney, Australasia Sydney New South Wales Australia
Sydney, Australia Sydney New South Wales Australia
Sydney, Australia – ‰`<\, ³o'Y)RšN Sydney New South Wales Australia
Sydney, Australia (usually) Sydney New South Wales Australia
Sydney, Australia :P Sydney New South Wales Australia
Sydney, Australia, Down Under Sydney New South Wales Australia
Sydney, Australia. Sydney New South Wales Australia
SYDNEY, Australiaaaa ! Sydney New South Wales Australia
Sydney, Coffs, Newie Coffs Harbour New South Wales Australia
Sydney, End of the world. Sydney New South Wales Australia
Sydney, etc. Sydney New South Wales Australia
Sydney, finally home Sydney New South Wales Australia
Sydney, Las Vegas, London Sydney New South Wales Australia
Sydney, London, Haarlem Sydney New South Wales Australia
Sydney, Melbourne and Perth Perth Western Australia Australia
Sydney, New South Wales Sydney New South Wales Australia
Sydney, NSW Sydney New South Wales Australia
Sydney, NSW, Australia Sydney New South Wales Australia
Sydney, NSW, Australia. Sydney New South Wales Australia
Sydney, Oz Sydney New South Wales Australia
Sydney, sorry I mean Canberra Canberra Australian Capital Territory Australia
Sydney, Straya Sydney New South Wales Australia
Sydney, Tonga, New Zealand Sydney New South Wales Australia
SYDNEY,AUSTRALIA Sydney New South Wales Australia
Sydney/Central Coast Sydney New South Wales Australia
Sydney/New York Sydney New South Wales Australia
Sydney/Newcastle, Australiaaa Sydney New South Wales Australia
Sydney: www.norepublic.com.au Sydney New South Wales Australia
SydneyLondon Sydney New South Wales Australia
Sydney’s East Randwick New South Wales Australia
Sydney’s Eastern Suburbs Randwick New South Wales Australia
Sydney’s Eastern Suburbs Sydney New South Wales Australia
Sydneyside Sydney New South Wales Australia
sydneysydney. Sydney New South Wales Australia
sydneyy Sydney New South Wales Australia
Syn Sity Sydney New South Wales Australia
SYNDEY, Australia :) Sydney New South Wales Australia
Syracus, NY Syracus New York United States
Tacoma, WA Tacoma Washington United States
Tallahassee, FL Tallahassee Florida United States
Tampa Tampa Florida United States
Tampa Bay Tampa Bay Florida United States
Tampa Bay, FL Tampa Bay Florida United States
Tampa Bay, Florida Tampa Bay Florida United States
tampa fl Tampa Florida United States
Tampa, FL Tampa Florida United States
Tampa, FL Tampa Florida United States
Tampa, Florida Tampa Florida United States
Tarneit, Victoria, Australia Tarneit Victoria Australia
tas   Tasmania Australia
Tasmania   Tasmania Australia
Tasmania (Australia)   Tasmania Australia
Tasmania, Australia   Tasmania Australia
Tasmania, Horse Trailer   Tasmania Australia
tassie   Tasmania Australia
Tatura, Australia Tatura Victoria Australia
Tecoma, Vic, Australia Tecoma Victoria Australia
Tehran Tehran   Iran
Tel Aviv, Israel     Israel
Templestowe Australia Templestowe Victoria Australia
Tenino, WA     United States
Tennessee   Tennessee United States
Tennessee, USA   Tennessee United States
Tennessee, USA     United States
Teo/Santiago, Spain Santiago   Spain
Terang – Australia Terang Victoria Australia
Terra Australis     Australia
Terra Australis Incognita     Australia
Texas   Texas United States
Texas     United States
TEXAS (DFW area)     United States
Texas by choice ex Californian   Texas United States
Texas GOP Primary Districts   Texas United States
Texas Hill Country   Texas United States
TEXAS!     United States
TEXAS!!!!!!!!!!!!!!!!!!!!     United States
Texas,     United States
TEXAS+USA   Texas United States
The 303 A-Town, Colorado     United States
the Atl Atlanta Georgia United States
THE BAY, USA     United States
The Best Coast     United States
the big green part     Unknown
The biosphere     Unknown
The Boiling North Cairns Queensland Australia
The Can     Unknown
The Couch     Unknown
the deepest circle of hell     Unknown
The Digital Coast     Unknown
The Doghouse     Unknown
The Fame Factory     Unknown
The Fortress of Barnitude, NYC New York City New York United States
The fun is where I am!     Unknown
The Global village     Unknown
The Gul (Victoria, Aus) Ferntree Gully Victoria Australia
The home of GAYFL     Australia
The Hunter Region, Australia Newcastle New South Wales Australia
The intergalactic web     Unknown
The Internet     Unknown
The island of Monaco     Monaco
The Jonex Mine 8 11 B Asteroid     Unknown
The Lake and Vegas baby!! Las Vegas Nevada United States
The Land Down Under     Australia
The land down under =]     Australia
the land down under.     Australia
the land of AFL Melbourne Victora Australia
The Land of Oz     Australia
The Left Coast     United States
The locker room     Unknown
THE LONE STAR STATE, TEXA$   Texas United States
THe Mecca Of The World     Unknown
The Murky World of Politics     Unknown
The NASH Hotel, Geelong Geelong Victoria Australia
the raven     Unknown
The Razor’s Edge:-~     Unknown
The Republic of Texas     United States
the room of requirement.     Unknown
The Sahara of the Bozart, USA     United States
The Shire     Australia
The South Bay California USA   California United States
The United States of America     United States
The Valley San Francisco California United States
The Web     Unknown
The wonderful land of Oz     Australia
The wonderful land of Oz.     Australia
The Woodlands, TX   Texas United States
The World     Unknown
The World by way of Las Vegas Las Vegas Nevada United States
The world is my oyster !     Unknown
The World!     Unknown
TheCandyCaveBitch     Unknown
This Pale Blue Dot.     Australia
This Planet .     Unknown
this sphere     Unknown
Thousand Oaks, CA Thousand Oaks California United States
Thousand Oaks, California USA Thousand Oaks California United States
thousands of tiny birds     United States
Three Bridges, Hunterdon, NJ Three Bridges New Jersey United States
Tigerland Richmond Victoria Australia
Time     Unknown
Times Square, USA New York City New York United States
TN   Tennessee United States
Today: Sydney, Sydney New South Wales Australia
Tokorozawa, japan Tokorozawa   Japan
Tokyo Tokyo   Japan
Tokyo JAPAN Tokyo   Japan
Toledo, Ohio & CA Toledo Ohio United States
Toorak, Victoria AU Toorak Victoria Australia
Toowoomba Toowoomba Queensland Australia
Toowoomba QLD Toowoomba Queensland Australia
Toowoomba, Qld Toowoomba Queensland Australia
Toowoomba, Queensland Toowoomba Queensland Australia
Toronto Toronto Ontario Canada
Toronto, Canada Toronto Ontario Canada
Toronto, ON Toronto Ontario Canada
Toronto, ON Canada     Canada
Toronto, Ontario Toronto Ontario Canada
Toronto, Ontario Canada Toronto Ontario Canada
Toronto, Ontario, Canada Toronto Ontario Canada
Toronto, sometimes Kingston Toronto Ontario Canada
Torquay Australia Torquay Victoria Australia
Torquay, Australia Torquay Victoria Australia
Torquay, Victoria Torquay Victoria Australia
Torquay, Victoria, Australia Torquay Victoria Australia
Torquay-Victoria Torquay Victoria Australia
Torrance, Ca Torrance California United States
Torrance,CA USA Torrance California United States
Townsville Townsville Queensland Australia
Townsville QLD, Australia Townsville Queensland Australia
Townsville, Australia Townsville Queensland Australia
Townsville, Australia. Townsville Queensland Australia
Townsville, North Queensland, Townsville Queensland Australia
Townsville, QLD Townsville Queensland Australia
Townsville, QLD, Australia Townsville Queensland Australia
Townsville, The Land of Aus Townsville Queensland Australia
Trafalgar, Victoria AU Trafalgar Victoria Australia
Traralgon, Vic Traralgon Victoria Australia
Treasure Coast, FL Treasure Coast Florida United States
Tremont, Illinois Tremont Illinois United States
Tri Cities WA & FOLLOW U 2 Benton Washington United States
True Blue Terriers     Unknown
Truro, SA, Australia Truro South Australia Australia
Tucson, Arizona Tucson Arizona United States
Tucson, AZ Tucson Arizona United States
Tuggeranong, Australia Tuggeranong Australian Capital Territory Australia
tugun qld Tugun Queensland Australia
Tulsa, OK Tulsa Oklahoma United States
Tuscaloosa, Alabama Tuscaloosa Alabama United States
tweetn in ur parliamentz Canberra Australian Capital Territory Australia
Twin Cities, MN USA St. Paul Minnesotta United States
TWiT Cottage, Petaluma. CA Petaluma California United States
Twitter     Unknown
Twitterverse     Unknown
U.S.     United States
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UK     United Kingdom
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UK or Australia     Australia
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Undisclosed     United States
UNHQ, New York New York City New York United States
Uni of New England, Australia Armidale New South Wales Australia
United Kingdom     United Kingdom
United State of America     United States
United States     United States
United States of America     United States
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Upper Left Coast     United States
UpState New York   New York United States
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USA (Support Arizona!)     United States
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USA- MN   Minnesotta United States
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ÜT: 19.053002,72.830552 Bombay Maharashtra India
ÜT: 19.111431,72.823609 Bombay Maharashtra India
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ÜT: 21.3069444, -157.8583333 Honolulu Hawaii United States
ÜT: -22.812002,148.697141 Middlemount Queensland Australia
ÜT: -22.957862,-43.196568   RJ Brazil
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ÜT: -23.000791,-43.344142   RJ Brazil
ÜT: -23.550304,-46.653495 São Paulo   Brazil
ÜT: 25.237559,55.328979 Dubai   United Arab Emirates
ÜT: 25.417262,55.468827   Ajman United Arab Emirates
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ÜT: 26.277149,-80.094348 Lighthouse Point Florida United States
ÜT: 26.671757,-80.204607 Royal Palm Beach Florida United States
ÜT: -26.804636, 153.130349 Caloundra Queensland Australia
ÜT: -27.209685,152.994282 North Lakes Queensland Australia
ÜT: -27.447128,152.939925 The Gap Queensland Australia
ÜT: -27.46758, 153.027892 Brisbane Queensland Australia
ÜT: -27.472296,153.03582 Kangaroo Point Queensland Australia
ÜT: -27.506852,153.010581 Yeronga Queensland Australia
ÜT: 27.735114,-82.351888 Ruskin Florida United States
ÜT: 27.949436, -82.4651441 Tampa Florida United States
ÜT: -28.00228, 153.431052 Surfers Paradise Queensland Australia
ÜT: -28.235976,153.562041 Chinderah New South Wales Australia
ÜT: 28.719177,-81.534865 Apopka Florida United States
ÜT: 29.072029,-82.062656 Belleview Florida United States
ÜT: 29.283611,-81.125466 Ormond Beach Florida United States
ÜT: 29.743639,-95.457766 Houston Texas United States
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ÜT: 29.986932,-95.348003 Houston Texas United States
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ÜT: 30.024547,-90.020469 New Orleans Louisiana United States
ÜT: 30.330819,-81.649665 Jacksonville Florida United States
ÜT: 30.825024,-83.319177 Valdosta Georgia United States
ÜT: 31.71745,35.193848     Israel
ÜT: -31.916326,115.895525 Bedford Western Australia Australia
ÜT: -31.937248,115.934061 Redcliffe Western Australia Australia
ÜT: -31.948318,115.788797 Floreat Western Australia Australia
ÜT: -31.959105,115.876879 East Perth Western Australia Australia
ÜT: -31.96471,115.820624 Kings Park Washington United States
ÜT: -32.659644,151.587895 Mindaribba New South Wales Australia
ÜT: -32.692473,151.578627 Bolwarra Heights New South Wales Australia
ÜT: -32.729383,151.535839 Telarah New South Wales Australia
ÜT: -32.729383,151.535839 Telarah New South Wales Australia
ÜT: 32.794302,-79.945317 Charleston South Carolina United States
ÜT: 32.802955, -96.769923 Dallas Texas United States
ÜT: -32.926357, 151.78122 Newcastle New South Wales Australia
ÜT: -32.927979,151.755002 Hamilton East New South Wales Australia
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ÜT: 33.006727,-96.672322 Plano Texas United States
ÜT: 33.295375,-111.899256 Chandler Arizona United States
ÜT: 33.364073,-111.827151 Gilbert Arizona United States
ÜT: 33.433133,-86.703553 Birmingham Alabama United States
ÜT: 33.50084,-84.392747 Riverdale Georgia United States
ÜT: 33.660297, -117.9992265 Huntington Beach California United States
ÜT: 33.674275,-117.837275 Irvine California United States
ÜT: -33.706883,151.201749 Belrose New South Wales Australia
ÜT: -33.746963,150.82831 Plumpton New South Wales Australia
ÜT: -33.746963,150.82831 Plumpton New South Wales Australia
ÜT: 33.767175,-84.401889 Atlanta Georgia United States
ÜT: 33.781365,-84.350283 Atlanta Georgia United States
ÜT: 33.787488,-84.384329 Atlanta Georgia United States
ÜT: -33.787934,150.911276 Blacktown New South Wales Australia
ÜT: -33.791303,151.090916 Eastwood New South Wales Australia
ÜT: 33.794601,-84.148157 Stone Mountain Georgia United States
ÜT: -33.813142,151.192926 Naremburn New South Wales Australia
ÜT: -33.82251,151.197083 Naremburn New South Wales Australia
ÜT: 33.837678,-118.183408 Long Beach California United States
ÜT: -33.848381,151.266711 Vaucluse New South Wales Australia
ÜT: -33.849053,151.053355   New South Wales Australia
ÜT: 33.851913,-84.38021 Atlanta Georgia United States
ÜT: 33.860328,-84.245527 Tucker Georgia United States
ÜT: -33.865893,151.091396 Strathfield New South Wales Australia
ÜT: -33.866018,151.208686 Sydney New South Wales Australia
ÜT: -33.867139, 151.207114 Sydney New South Wales Australia
ÜT: -33.872365,151.19117 Pyrmont New South Wales Australia
ÜT: -33.876949,151.213664 Darlinghurst New South Wales Australia
ÜT: -33.880637,151.208109 Haymarket New South Wales Australia
ÜT: -33.884726,151.205158 Haymarket New South Wales Australia
ÜT: -33.893798,151.205046 Redfern New South Wales Australia
ÜT: -33.912193,151.249817 Coogee New South Wales Australia
ÜT: 33.926903,-117.900016 Brea California United States
ÜT: 33.927399,-118.406763 El Segundo California United States
ÜT: 33.93147,-118.410991 Los Angeles California United States
ÜT: 33.949403,-118.398831 Los Angeles California United States
ÜT: 33.957809,-84.244132 Norcross Georgia United States
ÜT: -33.963175,151.090517 Penshurst New South Wales Australia
ÜT: 33.982003,-118.450898 Marina del Rey California United States
ÜT: 33.985365,-118.396649 Los Angeles California United States
ÜT: 33.992705,-118.476319 Los Angeles California United States
ÜT: 34.011664,-81.026304 Columbia South Carolina United States
ÜT: 34.028808,-117.921391 La Puente California United States
ÜT: 34.044817,-118.311893 Los Angeles California United States
ÜT: 34.054954,-118.393644 Los Angeles California United States
ÜT: 34.068647,-118.40866 Beverly Hills California United States
ÜT: 34.075736,-118.301746 Los Angeles California United States
ÜT: 34.079039,-81.155479 Columbia South Carolina United States
ÜT: 34.083638,-118.381336 West Hollywood California United States
ÜT: 34.084851,-118.384338 West Hollywood California United States
ÜT: 34.101479,-118.316028 Los Angeles California United States
ÜT: 34.10437,-118.31638 Los Angeles California United States
ÜT: 34.109553,-118.336445 Los Angeles California United States
ÜT: 34.112765,-118.336037 Los Angeles California United States
ÜT: 34.127937,-118.065575 Arcadia California United States
ÜT: 34.153265,-118.360663 San Fernando Valley California United States
ÜT: 34.153564,-118.346894 Burbank California United States
ÜT: 34.156066,-118.450035 Sherman Oaks California United States
ÜT: 34.156663,-118.644009 Calabasas California United States
ÜT: 34.194806,-118.649996 West Hills California United States
ÜT: 34.204043,-118.673387 West Hills California United States
ÜT: 34.2473,-118.53797 San Fernando Valley California United States
ÜT: -34.791539,138.703907 Golden Grove South Australia Australia
ÜT: -34.92577, 138.599732 Adelaide South Australia Australia
ÜT: -35.029993,138.567191 Bedford Park South Australia Australia
ÜT: 35.079068,-90.039163 Memphis Tennesse United States
ÜT: 35.139354,-80.639872 Stallings North Carolina United States
ÜT: -35.143015,138.479489 Port Noarlunga South Australia Australia
ÜT: 35.172519,-84.879804 Cleveland Tennesse United States
ÜT: 35.21146,-80.804384 Charlotte North Carolina United States
ÜT: -35.312735,148.910885 Duffy Australian Capital Territory Australia
ÜT: 35.549563,-77.361335 Winterville North Carolina United States
ÜT: 35.611699,-75.47012 Kinnakeet North Carolina United States
ÜT: 35.727036,-86.883021 Spring Hill Tennesse United States
ÜT: 35.829991,-86.455247 Murfreesboro Tennessee United States
ÜT: 35.975487,-115.141709 Henderson Nevada United States
ÜT: 36.075057,-115.144757 Las Vegas Nevada United States
ÜT: -36.082137, 146.910174 Albury New South Wales Australia
ÜT: 36.105809,-115.164077 Las Vegas Nevada United States
ÜT: 36.116801,-115.204872 Las Vegas Nevada United States
ÜT: 36.121203,-115.207135 Las Vegas Nevada United States
ÜT: 36.130412,-115.14781 Las Vegas Nevada United States
ÜT: 36.174247,-115.333071 Las Vegas Nevada United States
ÜT: 36.283016,-119.366711 Visalia California United States
ÜT: 36.406215,-5.190497 Estepona   Spain
ÜT: 36.594901,-82.1861 Bristol Tennesse United States
ÜT: 36.959791,-121.88602 Aptos California United States
ÜT: -36.99601,145.142151 Seymour Victoria Australia
ÜT: 37.330494,-76.748013 Williamsburg Virginia United States
ÜT: 37.456384,-122.22539 Redwood City California United States
ÜT: -37.552337,143.821768 Lake Wendouree Victoria Australia
ÜT: -37.563318, 143.863715 Ballarat Victoria Australia
ÜT: -37.567136,143.861333 Golden Point Victoria Australia
ÜT: -37.6597,144.456798 Merrimu Victoria Australia
ÜT: -37.660314, 144.441634 Darley Victoria Australia
ÜT: -37.689766,145.113154 Greensborough Victoria Australia
ÜT: -37.693264,145.102687 Greensborough Victoria Australia
ÜT: -37.712957,144.829062 Keilor Victoria Australia
ÜT: 37.721898,-97.24561 Wichita Kansas United States
ÜT: 37.762692,-122.4259 San Francisco California United States
ÜT: -37.764829, 144.943778 Brunswick West Victoria Australia
ÜT: 37.766285,-77.114228 Acquinton Virginia United States
ÜT: 37.783601,-122.399911 San Francisco California United States
ÜT: 37.784495,-122.393847 San Francisco California United States
ÜT: 37.786848,-122.409701 San Francisco California United States
ÜT: -37.788097,144.980881 Fitzroy North Victoria Australia
ÜT: -37.788311,144.982123 Fitzroy North Victoria Australia
ÜT: -37.791277,145.018454 Fairfield Victoria Australia
ÜT: -37.795508,144.990641 Collingwood Victoria Australia
ÜT: 37.795917,-122.39966 San Francisco California United States
ÜT: -37.808346,144.962897 Melbourne Victoria Australia
ÜT: -37.811111,144.966492 Melbourne Victoria Australia
ÜT: -37.814251, 144.963169 Melbourne Victoria Australia
ÜT: -37.814487,145.231363 Ringwood Victoria Australia
ÜT: -37.815345,144.966459 Melbourne Victoria Australia
ÜT: -37.816829,145.009069 Richmond Victoria Australia
ÜT: -37.824917,145.12128 Box Hill Victoria Australia
ÜT: -37.842017,145.011325 Toorak Victoria Australia
ÜT: -37.849601,145.049371 Glen Iris Victoria Australia
ÜT: -37.854722,144.684854 Hoppers Crossing Victoria Australia
ÜT: -37.859106,145.045747 Glen Iris Victoria Australia
ÜT: -37.86377,144.701082 Hoppers Crossing Victoria Australia
ÜT: -37.883041,144.996637 Ripponlea Victoria Australia
ÜT: -37.933497, 145.038504 Moorabbin Victoria Australia
ÜT: -37.950014,145.079012 Heatherton Victoria Australia
ÜT: -37.976293, 145.257787 Endeavour Hills Victoria Australia
ÜT: -37.992593,145.150692 Keysborough Victoria Australia
ÜT: -38.11122, 145.268832 Cranbourne Victoria Australia
ÜT: -38.14729, 144.360735 Geelong Victoria Australia
ÜT: -38.156352,144.354495 South Geelong Victoria Australia
ÜT: -38.156538, 145.123524 Frankston South Victoria Australia
ÜT: -38.159548,144.350817 South Geelong Victoria Australia
ÜT: -38.190871,144.470579 Leopold Victoria Australia
ÜT: -38.243963,143.195279   Victoria Australia
ÜT: -38.351032, 141.587701 Portland Victoria Australia
ÜT: 38.409935,-85.580262 Goshen Kentucky United States
ÜT: 38.593664,-76.541263 Huntingtown Maryland United States
ÜT: 38.645876,-121.50826 Sacramento California United States
ÜT: 38.736698,-121.276484 Roseville California United States
ÜT: 38.756303,-76.655048 Dunkirk Maryland United States
ÜT: 38.801248,-90.700271 O’Fallon Missouri United States
ÜT: 38.855033,-76.745044 Upper Marlboro Maryland United States
ÜT: 38.855033,-76.745044 Upper Marlboro Maryland United States
ÜT: 38.883103,-77.110875 Arlington Virginia United States
ÜT: 38.898615,-77.046835 Washington DC United States
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ÜT: 38.934716,-77.186654 McLean Virginia United States
ÜT: 38.991698,-77.03542 Silver Spring Maryland United States
ÜT: 38.991698,-77.03542 Silver Spring Maryland United States
ÜT: 39.02883,-77.502082 Ashburn Virginia United States
ÜT: 39.067812,-76.966541 Silver Spring Maryland United States
ÜT: 39.205246,-76.855488 Columbia Maryland United States
ÜT: 39.403949,-76.782996 Owings Mills Maryland United States
ÜT: 39.497073,-87.404956 Terre Haute Indiana United States
ÜT: 39.735004,-104.959847 Denver Colorado United States
ÜT: 39.761531,-86.166179 Indianapolis Indiana United States
ÜT: 39.895722,-75.428423 Media Pennsylvania United States
ÜT: 39.952514,-75.171054 Philadelphia Pennsylvania United States
ÜT: 39.961354,-75.154784 Philadelphia Pennsylvania United States
ÜT: 39.969812,-82.982551 Columbus Ohio United States
ÜT: 40.126556,-75.346999 Norristown Pennsylvania United States
ÜT: 40.233985,-75.626479 Pottstown Pennsylvania United States
ÜT: 40.430914,-74.215512 Keyport New Jersey United States
ÜT: 40.49898,-74.44071 New Brunswick New Jersey United States
ÜT: 40.585684,-111.829904 Sandy Utah United States
ÜT: 40.658097,-111.507355 Park City Utah United States
ÜT: 40.678182,-73.883522 Brooklyn New York United States
ÜT: 40.688334,-74.175046 Newark New Jersey United States
ÜT: 40.688701,-74.163884 Newark New Jersey United States
ÜT: 40.704453,-74.001289 New York New York United States
ÜT: 40.712288,-73.981188 New York New York United States
ÜT: 40.716563,-73.99647 New York New York United States
ÜT: 40.717782,-74.006142 New York New York United States
ÜT: 40.719043,-73.947169 Brooklyn New Jersey United States
ÜT: 40.72006,-73.981341 New York New York United States
ÜT: 40.730469,-73.993016 New York New York United States
ÜT: 40.730949,-74.175147 Newark New Jersey United States
ÜT: 40.735225,-74.006791 New York New York United States
ÜT: 40.742496,-74.008582 New York New York United States
ÜT: 40.744559,-73.99515 New York New York United States
ÜT: 40.746078,-74.001362 New York New York United States
ÜT: 40.758171,-73.977898 New York New York United States
ÜT: 40.759398,-73.987782 New York New York United States
ÜT: 40.764068,-73.976541 New York New York United States
ÜT: 40.766092,-73.976579 New York New York United States
ÜT: 40.767922,-73.987568 New York New York United States
ÜT: 40.768518,-74.402353 Madison New Jersey United States
ÜT: 40.773071,-73.994803 New York New York United States
ÜT: 40.773668,-73.981961 New York New York United States
ÜT: 40.776115,-82.407116 Mansfield Ohio United States
ÜT: 40.818875,-81.382653 Canton Ohio United States
ÜT: 40.897746,-73.839298 Mt Vernon New York United States
ÜT: 40.947486,-73.876102 Yonkers New York United States
ÜT: 41.05062,-73.981979 Pearl River New York United States
ÜT: 41.059741,-73.697563 Rye Brook New York United States
ÜT: 41.135479,-73.254939 Fairfield Connecticut United States
ÜT: 41.273614,-72.751469 Branford Connecticut United States
ÜT: 41.75398,-87.725057 Chicago Illinois United States
ÜT: 41.84106,-87.942346 Oak Brook Illinois United States
ÜT: 41.956749,-85.005971 Coldwater Michigan United States
ÜT: 42.121917,-88.300052 Carpentersville Illinois United States
ÜT: 42.579178,-76.214574 Cortland New York United States
ÜT: 42.579178,-76.214574 Cortland New York United States
ÜT: 43.095682,-79.032876 Niagara Falls New York United States
ÜT: 43.135041,-87.906115 Fox Point Wissconsin United States
ÜT: 43.542027,-80.247017 Guelph Ontario Canada
ÜT: 43.65408,-79.380191 Toronto Ontario Canada
ÜT: 43.660133,-79.35195 Toronto Ontario Canada
ÜT: 43.726321,7.411311 Cap-d’Ail Provence-Alpes-Côte d’Azur France
ÜT: 43.746215,-81.694097 Ashfield-Colborne-Wawanosh Ontario Canada
ÜT: 43.8041334, -120.5542012 Houma Oregon United States
ÜT: 43.875278,8.017421 Imperia Liguria Italy
ÜT: 44.138214,-88.510167 Neenah Wissconsin United States
ÜT: 44.199113,-120.629361 Crooked River Oregon United States
ÜT: 44.765096,8.643834 Predosa Piedmont Italy
ÜT: 44.765096,8.643834 Predosa   Italy
ÜT: 44.936168,-93.096706 St Paul Minnesotta United States
ÜT: 44.938602,-93.319329 Minneapolis Minnesotta United States
ÜT: 45.482021,-111.256448 Gallatin Gateway Montana United States
ÜT: 45.495882,-73.581221 Montreal Quebec Canada
ÜT: 45.535014,-122.39956 Troutdale Oregon United States
ÜT: 45.785545,11.745171 Romano d’Ezzelino Veneto Italy
ÜT: 46.87648,-96.38493 Hawley Minnesotta United States
ÜT: 47.6062095, -122.3320708 Seattle Washington United States
ÜT: 47.614571,-122.31891 Seattle Washington United States
ÜT: 48.891615,2.385504 Paris Ile-de-France France
ÜT: 48.891615,2.385504 Paris   France
ÜT: 48.995377,2.517694 Roissy-en-France Ile-de-France France
ÜT: 49.229642,-122.843212 Coquitlam British Columbia Canada
ÜT: 49.904034,-97.164567 Winnipeg Manitoba Canada
ÜT: 51.065803,6.566164 Grevenbroich North Rhine-Westphalia Denmark
ÜT: 51.071055,-114.154817 Calgary Alberta Canada
ÜT: 51.154446,-2.541567     United Kingdom
ÜT: 51.462686,-0.138878 Lambeth England United Kingdom
ÜT: 51.462686,-0.138878 Lambeth Greater London United Kingdom
ÜT: 51.502957,-0.126003 London Greater London United Kingdom
ÜT: 51.754792,-1.269904     United Kingdom
ÜT: 53.374464,-1.339104   Sheffield United Kingdom
ÜT: 53.516669,-2.184229 Manchester England United Kingdom
ÜT: 56.480363,-2.866407 Dundee Dundee City United Kingdom
ÜT: 58.213543,-136.513065 Hoonah-Angoon Alaska United States
ÜT: -6.127828,106.649705 Tangerang   Indonesia
ÜT: -6.15335,106.80603 Jakarta   Indonesia
ÜT: -6.15335,106.80603   Jakarta Capital Region Indonesia
ÜT: -6.235691,107.004012   West Java Indonesia
ÜT: -6.245664,106.832942   Jakarta Capital Region Indonesia
ÜT: -6.249004,106.818024   Jakarta Capital Region Indonesia
ÜT: -6.249057,106.824806   Jakarta Capital Region Indonesia
ÜT: -6.256761,106.814137   Jakarta Capital Region Indonesia
ÜT: -6.265353,107.013476   West Java Indonesia
ÜT: -6.2661,106.782066   Jakarta Capital Region Indonesia
ÜT: -6.27435,106.803554 Jakarta   Indonesia
ÜT: -6.274978,106.876041 Jakarta   Indonesia
ÜT: -6.274978,106.876041 Jakarta   Indonesia
ÜT: -6.284259,106.78053 Jakarta   Indonesia
ÜT: -6.308305,106.838256   Jakarta Capital Region Indonesia
ÜT: -6.99077,110.423172   Central Java Indonesia
ÜT: -7.068586,110.411736   Central Java Indonesia
ÜT: -8.728818,115.16906 Kuta Bali Indonesia
Utah, USA   Utah United States
Vacaville California Vacaville California United States
Valencia, Venezuela Valencia   Venezuela
Valley of the Sun, AZ, USA Valley of the Sun Arizona United States
Vancouver Vancouver British Columbia Canada
vancouver, bc, ca Vancouver British Columbia Canada
Vancouver, BC, Canada Vancouver British Columbia Canada
Vancouver, Canada Vancouver British Columbia Canada
Vancouver, WA Vancouver Washington United States
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Venezuela     Venezuela
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Venice Beach, California Venica Beach California United States
Venice, CA Venice California United States
Venice, California Venice California United States
Ventura County CA Ventura California United States
Ventura, California… beautif Ventura California United States
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Vermont, Melbourne Vermont Victoria Australia
Vermont, USA   Vermont United States
Versailles, France Versailles   France
Versailles, Heaven     Unknown
Viamão – RS – Brazil São Paulo   Brazil
VIC, Australia   Victoria Australia
Victoria   Victoria Australia
Victoria (Australia)   Victoria Australia
Victoria , australia   Victoria Australia
Victoria , Australia   Victoria Australia
Victoria Australia   Victoria Australia
Victoria, Aussie Land   Victoria Australia
Victoria, Australia   Victoria Australia
Victoria, Australia.   Victoria Australia
Victoria, Melbourne Melbourne Victoria Australia
Victoria, South Morang South Morang Victoria Australia
Victoria, Torquay Torquay Victoria Australia
Victoriaaaaaaa   Victoria Australia
victoria-australia   Victoria Australia
Vienna, Austria Vienna   Austria
Villa Park, Illinois Villa Park Illinois United States
vincentia, nsw, australia Vincentia New South Wales Australia
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Viva Bris Vegas Brisbane Queensland Australia
Viva BrisVegas Brisbane Queensland Australia
Vizag, India Vizag   India
Vote 1 Aus Sex Party in Senate     Australia
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WA, USA   Washington United States
Wageningen Wageningen   Netherlands
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WAGGA WAGGA NSW Wagga Wagga New South Wales Australia
Wagga, Australia Wagga Wagga New South Wales Australia
Wagin, WA Wagin Western Australia Australia
Wales   Wales United Kingdom
Wales, UK   Wales United Kingdom
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Waltham, MA Waltham Massachutess United States
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Warrenton, VA Warrenton Virginia United States
warrnambool Warrnambool Victoria Australia
Warrnambool .Vic. Australia Warrnambool Victoria Australia
Warrnambool, Victoria Warrnambool Victoria Australia
Was here now back then     Unknown
Washington   Washington United States
WASHINGTON   Washington United States
Washington (DC) Washington D.C.   United States
Washington D.C. Washington D.C.   United States
Washington DC Washington D.C.   United States
Washington DC, USA Washington D.C.   United States
Washington DC, Yo! Washington D.C.   United States
Washington State   Washington United States
Washington, D.C. Washington D.C.   United States
Washington, D.C. and NYC Washington D.C.   United States
Washington, DC Washington D.C.   United States
Washington, DC Washington, DC   United States
Washington, DC USA     United States
Wasilla, AK Wasilla Alaska United States
Wassenaar, NL Wassenaar   Netherlands
Watcha ya need 2 know 4     Unknown
Watching TV     Unknown
Waterloo, ON Waterloo Ontario Canada
Watertown, MA, USA Watertown Massachutess United States
Watts California Watts California United States
Waverley Park Melbourne Victoria Australia
Wayne, NJ Wayne New Jersey United States
Weeknights @ 11:35/10:35c New York City New York United States
Weeknights 12:35/11:35c New York City New York United States
Wellington Point, Australia Wellington Point Queensland Australia
Wellington, New Zealand Wellington Wellington New Zealand
Wellington, NZ Wellington Wellington New Zealand
Wembley, Perth, Australia Wembley Western Australia Australia
Wemyss St, Enmore, NSW 2042, Enmore New South Wales Australia
Were The Cats Run Free!     Unknown
werribee Werribee Victoria Australia
Werribee Melb VIC Aus   Victoria Australia
Werribee melbourne geelong Aus   Victoria Australia
Werribee Victoria Australia Werribee Victoria Australia
Werribee, Melbourne, Australia Werribee Victoria Australia
Werribee, Victoria Werribee Victoria Australia
west     Unknown
West Australia   Western Australia Australia
West Coast     United States
West Fargo, ND West Fargo North Dakota United States
West Hollywood, CA West Hollywood California United States
West Oz   Western Australia Australia
West Point, NY/Akron, OH Akron Ohio United States
West Texas OIL Country Odessa   Texas United States
West Virginia   West Virginia United States
Westbourne Park, SA, Australia Westbourne Park South Australia Australia
Westchester, NY Westchester New York United States
Westeren Victoria Horsham Victoria Australia
Western Australia   Western Australia Australia
Western AustraliAU, Manning Manning Western Australia Australia
Western Suburbs, Melbourne, AU Melton Victoria Australia
Western Sydney, NSW, Australia Penrith New South Wales Australia
Western USA     United States
Western Washington   Washington United States
Westlake, QLD (-27.550369,152. Westlake Queensland Australia
Westpac Centre Melbourne Victoria Australia
What a fun, sexy time for you     Unknown
What? Oh! Right here :-)     Unknown
Where art comes to die.     Unknown
where data flows     Unknown
where ever     Unknown
where ever u want me to be     Unknown
Where in the world are you?     Unknown
Where is my din din’s bowl?     Unknown
where no one dares to go     Unknown
Where Opportunities Arise!     Unknown
Where the Cash At     Unknown
where the heart is     Unknown
Where the SupaStars B     Unknown
where the wild things are     Unknown
where they ride kangaroos     Australia
Where women glow & men plunder     Unknown
Where You Are     Unknown
Whereever you are!     Unknown
wherelaaaand AUS     Australia
whereve lee pace is (:     Unknown
wherever i am, im prolly late     Unknown
Wherever I go, there I am.     Unknown
Wherever the story takes me     Unknown
Wherever the windmills blow…     Unknown
Wherever Wilfner is. :) ~*     Unknown
White America     United States
Why do you wanna know? Creep!     Unknown
Whyalla South Australia Whyalla South Australia Australia
Wide World of Sport     United States
Williamsport, PA Williamsport Pennsylvania United States
Willoughby Willoughby New South Wales Australia
Windsor, Melbourne   Victoria Australia
Windsor, Queensland, Australia Windsor Queensland Australia
Windsor, UK Windsor England United Kingdom
Windy Hill, Essendon Essendon Victoria Australia
Winnellie Australia Winnellie Northern Territory Australia
Winnipeg, Manitoba, Canada Winnipeg Manitoba Canada
Winston Salem Winston-Salem North Carolina United States
Wisbech Wisbech England United Kingdom
Wisconsin   Wisconsin United States
Wodonga, Australia Wodonga Victoria Australia
Wodonga, Victoria Wodonga Victoria Australia
Wolli Creek, NSW, Australia Wolli Creek New South Wales Australia
Wollongong Wollongong New South Wales Australia
Wollongong, Australia Wollongong New South Wales Australia
Wollongong, NSW Wollongong New South Wales Australia
Wollstonecraft (yuck!) Wollstonecraft New South Wales Australia
wombatville, Sydney Sydney New South Wales Australia
Wonderland with RB& Sasha Kate     Unknown
Wonderland…     Unknown
wonthaggi VIC Wonthaggi Victoria Australia
Wonthaggi, Victoria, Australia Wonthaggi Victoria Australia
Woodside South Australia Woodside South Australia Australia
Woolloongabba, Brisbane Woolloongabba Queensland Australia
Work/Home. That’s about it.     Unknown
WORLD     Unknown
World Wide Web     Unknown
World Citizen     Unknown
World square, Sydney Sydney New South Wales Australia
World Wide     Unknown
World Wide Web     Unknown
World~of~Food     Unknown
Worldcup     Belgium
Worldwide     Unknown
Worldwide News from Adelaide Adelaide South Australia Australia
Worldwide!     Unknown
Worldwide, China     China
Worldwide.     Unknown
Woy Woy, NSW, Australia Woy Woy New South Wales Australia
www.AnimalDrudge.com     Unknown
www.bet247.com.au     Australia
Yarm, Teesside, England     United Kingdom
yarra junction Yarra Junction Victoria Australia
Yass Yass New South Wales Australia
Yass, Australia Yass New South Wales Australia
Yeosu, South Korea Yeosu   South Korea
ÿø Walled Garden     Australia
Yorkshire & Cotswolds Yorkshire England United Kingdom
You’d like to know     Unknown
Your Mum’s House     Unknown
Your nearest watering hole     Australia
Your tweets     Unknown
Zone 1     Unknown

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Methodology: Draft/Free writing (part 3)

Posted by Laura on Friday, 27 August, 2010

My goal has been to write 200 words a day on this section this week. I haven’t quite gotten there but there is enough that I feel like I should post an update. I figured out how to get better cites for methodologies and definitions. I am having some issues on case studies as they pertain to case studies as my ideal is Australian sport or sport and social media. There aren’t always ones out there that work easily.


Methodology

When conducting social media research, there are ten general methods that can be used to gather and analyze data. These are:

  1. Individual case studies for how a business uses social media and the web;
  2. Search and traffic analytics analysis;
  3. Sentiment analysis and reputation management;
  4. Content analysis;
  5. Usability studies;
  6. Interaction and collaboration analysis;
  7. Relationship analysis to try to determine how people interact and to identify key influencers;
  8. Population studies;
  9. Online target analysis of behavior and psychographics; and
  10. Predictive analysis.

Each of these methods offers insights into various aspects of the web and its population. The type of analysis used is often specific to the purpose of the research, involved blended approaches from traditional analysis types, and different methods are often used in conjunction with each other. These methods often blend quantitative and qualitative analysis. Choosing the correct method of gathering analyzing data can be one of the biggest hurdles for being able to measure ROI and understand how a community works.

This section will provide a brief summary of each type, explain how to conduct this type of research and give examples that used that methodology.

Individual case studies for how a business uses social media and the web,

Case studies on social media usage are often done to measure the effectiveness of specific actions taken by an organization.

Bronwyn et al. (2005) say case studies “typically examine the interplay of all variables in order to provide as complete an understanding of an event or situation as possible. This type of comprehensive understanding is arrived at through a process known as thick description, which involves an in-depth description of the entity being evaluated, the circumstances under which it is used, the characteristics of the people involved in it, and the nature of the community in which it is located.”

This methodology often incorporates components of all the other methods discussed in this section. The specific methods often depend on the goals of the person or organization conducting the case study.

Vincenzini (2010) did a case study regarding the use of the social media by the NBA in an attempt to define why they have been successful in using it to promote the league. The author used quantitative analysis to measure the size of the community, the volume of content they were viewing on sites like YouTube and the volume of content they were creating on sites like Twitter. The quantitative analysis was synthesized with explanations from NBA employees to explain their practices in the context of their own business decisions as they pertained to social media. This was followed up with an explanation as to what worked and what did not worked and offered advise for others involved with sport and social media to help them leverage their own position.

Case studies are a mixed methodology approach, borrowing from other approaches. The major difference is that the case study focuses on a narrower perspective with the goal of tracking behavioral changes, or in advising others on how an organization changed practices and how those lessons can be applied elsewhere.

Search and traffic analytics analysis

Search engine and traffic analytics generally is done internally to determine how to optimize a site in order to increase the amount of visitors a site gets and the total number of pages that they view. This method involves identifying how people arrive at a specific site and the pages they visit while at the site. Traffic analytics analysis often includes six different components: Search engine visitors, paid search advertisements, pay per click, organic traffic, direct traffic and internal site traffic.

Ramos and Cota (2009) define traffic analytics as “Tools that analyze and compare customer activity in order to make business decisions and increase sales. Analytics tools can report the number of conversions, the keywords that brought conversions, the sites that sent converting traffic, conversion by campaign, and so on.”

There are a number of different tools that allow for this type of analysis. Kaushik (2010) recommends Google Analytics, a free tool that involves putting a bit of code on all pages of a site. Kaushik (2010) points out that various types of traffic analysis can be done using the various tools provided by Google Analytics. The author claims that Google Analytics allows you to break the analysis down into “three important pieces: campaign response, website behavior, and business outcomes.” (Kaushik 2010)

Sentiment analysis and reputation management

Sentiment analysis involves identifying content related to a topic and identifying the emotion connected to that content. In a sport context, sentiment analysis could involve determining if newspapers are providing positive or negative coverage of a team. In a social media context, sentiment analysis would involve determining attitudes being expressed on Twitter in individual tweets.

Sterne (2010) suggests that content being ReTweeted on Twitter can be seen as a tool to measure positive sentiment. Sterne (2010) suggests that the ratio of follows/followers is not an effective tool for measuring sentiment on Twitter.

Content analysis,

Content analysis involves looking at the individual components of something larger and analyzing it. In a social media context, the content could be comments on a Facebook fanpage, or all the tweets made by a person or group.

With content analysis, the researcher views data as “data as representation not of physical events but of texts, images and expressions that are created to be seen, read, interpreted, and acted on for their meanings, and therefore be analyzed with such uses in mind.” (Krippendorff 2007) Krippendorff (2007) defines the basic methodology used in content analysis as unitizing, sampling, recording, reducing, inferring, and narrating.

Usability studies,


Interaction and collaboration analysis,


Relationship analysis

Relationship analysis involves examining the relationships between users on a social network, message board or mailing list. The goal is to identify cliques of different sizes or people who are particularly influential in a particular group online. This type of analysis is important to many brands including Starbucks (Plimsoll, 2010). The purpose of relationship analysis is to identify key influencers and social who influencers who are or who have the potential to be brand evangelists. (Plimsoll, 2010)

Lord and Singh (2010) define social media influence marketing as being “about recognizing, accounting and tapping into the fact that as your potential consumer makes a purchasing decision, he or she is being influenced by different circles of people through conversations with them, both online and off.”

The methodology for influence identification is not clearly spelled out as identifying influencers can be heavily dependent on the network being examined and how the community on a specific site functions. As a result, social media marketers suggest an array of tools like Twitalyzer that can be used to help determine your own influence. (Ankeny 2009) Twitalyzer’s Peterson and Katz (2010) explain their site-specific method of determining influence as including the following variables: Engagement level, total followers, total following, hashtags cited, lists included on, frequency of updates, references by others, references of others, times content is retweeted, urls cited and a number of other variables. Sterne (2010) suggests using WeFollow.com to find people who use topic specific #hashtags on Twitter. The people who tweet the most about a topic are likely to be influencers in that others looking for tweets around a topic are likely to read them. In a wider web context, Sterne (2010) suggests using Technorati to identify bloggers who have clout and influence around a certain topic.

This type of research can be viewed as a fundamental component to sentiment analysis; social media marketing companies like Razorfish often package the two together. (Lord & Singh, 2010)

Population studies

Online target analysis of behavior and psychographics

Online targeting of and marketing towards a specific audience because of their demographic characteristics is extremely common on the Internet. Psychographics is a term that includes targeting towards a specific demographic group except it includes the offline component.

Sutherland and Canwell (2004) define psychographics as “market research and market segmentation technique used to measure lifestyles and to develop lifestyle classifications.” (p. 247) Nicolas (2009) defines online behaviorial analysis as a series of steps: Collecting user data across several sites, organizing information about users based on the sites they visit and their behavior on those sites, “infer demographics and interest data”, and classifying new users based on the collected data in order to deliver relevant ads and content based their demographic profiles. Kinney, McDaniel, and DeGaris (2008) define psychographics as attitude towards something such as a brand or involvement with an organization.

Given the methodology involved, much of this type of research involves action research in that it is done in a specific content, based on internal models to address specific situations.

An example of this type of research was done by Kinney, McDaniel, and DeGaris (2008) who investigated the demographic characteristics of NASCAR fans and their attitudes towards NASCAR, its sponsors and sponsor involvement with NASCAR. The research found that age, gender and education were all important variables in determining sponsor recall: Younger, more educated males had the best brand recall amongst NASCAR fans.

This type of research can be viewed as a subcomponent of a population study in that demographic information is sought about the population. In an online context, it often works in conjunction with search and traffic analytics analysis, content analysis, and interaction and collaboration analysis.

Predictive analysis

A search on 13 July 2010 on SPORTDiscus had three results for “predictive analysis.” A search on the same date on Scopus had 605 results, 275 of which were in engineering, 132 in computer science and 102 in medicine. Predictive analysis is probably one of the least used analysis methods, especially in social media and fandom.

What is predictive analysis? At its simplest, it is identifying a future event or events, monitoring selection actions that precede the event and seeing if those events can be used to predict the outcome of similar events in the future. If a predictive value is found, an organization can monitor behaviors to help make more informed decisions.

An example of this type of research is “Predicting the Future With Social Media” by Asur and Huberman (2010). Their goal was to determine if tweet volume and sentiment on Twitter prior to a movie being released could be used to predict how well a movie performs at the box office. Their methodology involved identifying movie wider release dates that took place on a Friday, creating a list of keyword searches related to those movies, and using the Twitter API to collect all tweets and aggregate date that mention those keywords over a three month time period. The authors then compared the tweet volume to box office performance. They concluded that social media “can be used to build a powerful model for predicting movie box-office revenue.” (Asur & Huberman, 2010)

This type of research can be used in conjunction with other methods. It can be used along side a population study to see if certain actions will result in demographic changes.
Ankeny, J. (2009). HOW TWITTER IS REVOLUTIONIZING BUSINESS. Entrepreneur, 37(12), 26-32. Retrieved from Business Source Premier database.

Asur, S., & Huberman, B. A. (2010). Predicting the Future With Social Media. Social Computing Lab. Retrieved from http://www.hpl.hp.com/research/scl/papers/socialmedia/socialmedia.pdf

Bronwyn, B., Dawson, P., Devine, K., Hannum, C., Hill, S., Leydens, J., Matuskevich, D. Traver, C, and Palmquist, M. (2005). Case Studies. Writing@CSU. Colorado State University Department of English. Retrieved August 26, 2010 from http://writing.colostate.edu/guides/research/casestudy/.

Kaushik, A. (2010). Web analytics 2.0: The art of online accountability & science of customer centricity. Hoboken, N.J: Wiley.

Kinney, L., McDaniel, S., & DeGaris, L. (2008). Demographic and psychographic variables predicting NASCAR sponsor brand recall. International Journal of Sports Marketing & Sponsorship, 9(3), 169-179. Retrieved from SPORTDiscus with Full Text database.

Krippendorff, K. (2007). Content analysis: An introduction to its methodology. Thousand Oaks, Calif.

Lord, B., & Singh, S. (2010). Fluent: The Razorfish Social Influence Marketing Report. Razorfish. Retrieved August 25, 2010, from http://fluent.razorfish.com/publication/?m=6540&l=1

Nicolas, P. (2009, December 17). “Online audience behavior analysis and targeting.” Patrick Nicolas Official Home Page. Retrieved August 1, 2010, from http://www.pnexpert.com/Analytics.html

Peterson, E., & Katz, J. (2010). Twitalyzer Help and Company Information | Twitalyzer: Serious Analytics for Social Media and Social CRM. Twitalyzer. Retrieved August 25, 2010, from http://www.twitalyzer.com/help.asp

Plimsoll, S. (2010). Find and target customers in the social media maze. Marketing (00253650), 10-11. Retrieved from Business Source Premier database.

Ramos, A., & Cota, S. (2009). Search engine marketing. New York: McGraw-Hill.

Sterne, J. (2010). Social media metrics: How to measure and optimize your marketing investment. Hoboken, N.J: John Wiley.

Sutherland, J., & Canwell, D. (204). Key Concepts in Marketing. Palgrave Key Concepts. Hampshire, England: Palgrave MacMillan.

Vincenzini, A. (2010, July 14). A Case Study: The NBA’s Social Media Strategy & Tactics. Retrieved August 26, 2010, from http://www.slideshare.net/AdamVincenzini/the-nba-and-social-media-a-case-study

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Methodology: Draft/Free writing (part 2)

Posted by Laura on Wednesday, 18 August, 2010

This is an update of a draft of my methodology section. I finally got back to writing it as I took some time off from it and my literature review. This is very much a draft intended for me to explore concepts of what types of online research exist as it pertains to social media. After the major methods are spelled out, the intend is to go further into population studies, the why and how of them. This should be followed up with the methodology for what I’m actually doing. That last part will ultimately be the shortest as population studies aren’t that hard to do. The major methodology parts will actually be spelled out in the individual sections and will follow a format similar to ones that I’ve done for other posts here.

Methodology

When conducting social media research, there are ten general methods that can be used to gather and analyze data. These are:

1. Individual case studies for how a business uses social media and the web,
2. Search and traffic analytics analysis,
3. Sentiment analysis and reputation management,
4. Content analysis,
5. Usability studies,
6. Interaction and collaboration analysis,
7. Relationship analysis to try to determine how people interact and to identify key influencers, and
8. Population studies
9. Online target analysis of behavior and psychographics,
10. Predictive analysis.

Each of these methods offers insights into various aspects of the web and its population. The type of analysis used is often specific to the purpose of the research, involved blended approaches from traditional analysis types, and different methods are often used in conjunction with each other. These methods often blend quantitative and qualitative analysis. Choosing the correct method of gathering analyzing data can be one of the biggest hurdles for being able to measure ROI and understand how a community works.
This section will provide a brief summary of each type, explain how to conduct this type of research and give examples that used that methodology.

Online target analysis of behavior and psychographics

Online targeting of and marketing towards a specific audience because of their demographic characteristics is extremely common on the Internet. Psychographics is a term that includes targeting towards a specific demographic group except it includes the offline component.

Sutherland and Canwell (2004) define psychographics as “market research and market segmentation technique used to measure lifestyles and to develop lifestyle classifications.” (p. 247) Nicolas (2009) defines online behaviorial analysis as a series of steps: Collecting user data across several sites, organizing information about users based on the sites they visit and their behavior on those sites, “infer demographics and interest data”, and classifying new users based on the collected data in order to deliver relevant ads and content based their demographic profiles. Kinney, McDaniel, and DeGaris (2008) define psychographics as attitude towards something such as a brand or involvement with an organization.

Given the methodology involved, much of this type of research involves action research in that it is done in a specific content, based on internal models to address specific situations.

An example of this type of research was done by Kinney, McDaniel, and DeGaris (2008) who investigated the demographic characteristics of NASCAR fans and their attitudes towards NASCAR, its sponsors and sponsor involvement with NASCAR. The research found that age, gender and education were all important variables in determining sponsor recall: Younger, more educated males had the best brand recall amongst NASCAR fans.

This type of research can be viewed as a subcomponent of a population study in that demographic information is sought about the population. In an online context, it often works in conjunction with search and traffic analytics analysis, content analysis, and interaction and collaboration analysis.

Predictive analysis

A search on 13 July 2010 on SPORTDiscus had three results for “predictive analysis.” A search on the same date on Scopus had 605 results, 275 of which were in engineering, 132 in computer science and 102 in medicine. Predictive analysis is probably one of the least used analysis methods, especially in social media and fandom.

What is predictive analysis? At its simplest, it is identifying a future event or events, monitoring selection actions that precede the event and seeing if those events can be used to predict the outcome of similar events in the future. If a predictive value is found, an organization can monitor behaviors to help make more informed decisions.
An example of this type of research is “Predicting the Future With Social Media” by Asur and Huberman (2010). Their goal was to determine if tweet volume and sentiment on Twitter prior to a movie being released could be used to predict how well a movie performs at the box office. Their methodology involved identifying movie wider release dates that took place on a Friday, creating a list of keyword searches related to those movies, and using the Twitter API to collect all tweets and aggregate date that mention those keywords over a three month time period. The authors then compared the tweet volume to box office performance. They concluded that social media “can be used to build a powerful model for predicting movie box-office revenue.” (Asur & Huberman, 2010)

This type of research can be used in conjunction with other methods. It can be used along side a population study to see if certain actions will result in demographic changes.

References

Asur, S., & Huberman, B. A. (2010). Predicting the Future With Social Media. Social Computing Lab. Retrieved from http://www.hpl.hp.com/research/scl/papers/socialmedia/socialmedia.pdf

Kinney, L., McDaniel, S., & DeGaris, L. (2008). Demographic and psychographic variables predicting NASCAR sponsor brand recall. International Journal of Sports Marketing & Sponsorship, 9(3), 169-179. Retrieved from SPORTDiscus with Full Text database.

Nicolas, P. (2009, December 17). “Online audience behavior analysis and targeting.” Patrick Nicolas Official Home Page. Retrieved August 1, 2010, from http://www.pnexpert.com/Analytics.html

Sutherland, J., & Canwell, D. (204). Key Concepts in Marketing. Palgrave Key Concepts. Hampshire, England: Palgrave MacMillan.

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Methodology: Draft/Free writing (part 1)

Posted by Laura on Tuesday, 13 July, 2010

One of the ways I learn best is by talking through a problem.  I compare and contrast things.  I ask people their opinions.  I read things to people.  I get into in depth conversations about practices.  I ask questions.   Write now, I’m in the process of writing my methodology.  At this point, I feel like what I’m really doing trying to outline the current practices in social media research, explaining how they are done and giving examples.  Once that is done, I can justify using a population study as the analysis process to help answer my research question and then go into a bit more depth regarding that.  Outlining the available methods seems important because the processes can be a bit different than traditional sociology methods.  Or at least, it feels that way.  This will be updated as I go along.  How often that happens depends on my motivation to write.

Methodology

When conducting social media research, there are ten general methods that can be used to gather and analyze data.  These are:

1. Individual case studies for how a business uses social media and the web,
2. Search and traffic analytics analysis,
3. Sentiment analysis and reputation management,
4. Content analysis,
5. Usability studies,
6. Interaction and collaboration analysis,
7. Relationship analysis to try to determine how people interact and to identify key influencers, and
8. Population studies
9. Online target analysis of behavior and psychographics,
10. Predictive analysis.

Each of these methods offers insights into various aspects of the web and its population.  The type of analysis used is often specific to the purpose of the research, involved blended approaches from traditional analysis types, and different methods are often used in conjunction with each other.  These methods often blend quantitative and qualitative analysis.  Choosing the correct method of gathering analyzing data can be one of the biggest hurdles for being able to measure ROI and understand how a community works.
This section will provide a brief summary of each type, explain how to conduct this type of research and give examples that used that methodology.

Predictive analysis
A search on 13 July 2010 on SPORTDiscus had three results for “predictive analysis.” A search on the same date on Scopus had 605 results, 275 of which were in engineering, 132 in computer science and 102 in medicine. Predictive analysis is probably one of the least used analysis methods, especially in social media and fandom.
What is predictive analysis?  At its simplest, it is identifying a future event or events, monitoring selection actions that precede the event and seeing if those events can be used to predict the outcome of similar events in the future.  If a predictive value is found, an organization can monitor behaviors to help make more informed decisions.
An example of this type of research is “Predicting the Future With Social Media” by Asur and Huberman (2010).  Their goal was to determine if tweet volume and sentiment on Twitter prior to a movie being released could be used to predict how well a movie performs at the box office.  Their methodology involved identifying movie wider release dates that took place on a Friday, creating a list of keyword searches related to those movies, and using the Twitter API to collect all tweets and aggregate date that mention those keywords over a three month time period.  The authors then compared the tweet volume to box office performance.  They concluded that social media “can be used to build a powerful model for predicting movie box-office revenue.” (Asur & Huberman, 2010)
This type of research can be used in conjunction with other methods.  It can be used along side a population study to see if certain actions will result in demographic changes.

References

Asur, S., & Huberman, B. A. (2010). Predicting the Future With Social Media. Social Computing Lab. Retrieved from http://www.hpl.hp.com/research/scl/papers/socialmedia/socialmedia.pdf



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