Ozzie Sport

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.

Related Posts:

• Socceroos: A creative research data dump (part 1)
• Distribution of Australian sports fans by league and location
• The Impact of Jason Akermanis’s Comments on the Western Bulldogs’s Online Fanbase
• Data absent context can change the meaning: Did Julia Gillard hurt the Bulldogs?

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.

Related Posts:

• Version 3: Most popular Australian athlete and team related Twitter accounts by total followers.
• Popularity of Australian sports leagues across various social networks
• Most popular female Australian (and New Zealand) sport related Twitter accounts (November 24)
• Most popular Australian (and New Zealand) sport related Twitter accounts (November 24)

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.

Related Posts:

• Online Activity in the Wake of the Melbourne Storm Controversy
• Melbourne Storm and Wikipedia
• Most popular Australian (and New Zealand) sport related Twitter accounts (November 24)
• More popular Australian (and Kiwi) sport related Facebook fanpages and groups (November 23)

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.

Related Posts:

• Who is more sport mad (re: professional sport)? Americans or Aussies? Methodology question
• Methodology: Draft/Free writing (part 4)
• Location, location, location: Why can’t Twitter users standardize their locations?
• Methodology: Draft/Free writing (part 3)

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.

Related Posts:

• Most popular Australian (and New Zealand) sport related Twitter accounts (November 24)
• More popular Australian (and Kiwi) sport related Facebook fanpages and groups (November 23)
• More popular Australian (and Kiwi) sport related Facebook fanpages and groups
• Most popular Australian (and New Zealand) sport related Twitter accounts (November 18)

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:

• Who is more sport mad (re: professional sport)? Americans or Aussies? Methodology question
• Location, location, location: Why can’t Twitter users standardize their locations?
• Methodology: Draft/Free writing (part 3)
• Methodology: Draft/Free writing (part 2)

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 ” 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
çay1 r 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			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	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
Udaipur, Rajasthan INDIA	Udaipur		India
UK			United Kingdom
UK – Nottingham – London	London	England	United Kingdom
UK and USA			United States
UK or Australia			Australia
Ukraine			Ukraine
Ulsan, South Korea	Ulsan		South Korea
Ultimo, NSW	Ultimo	New South Wales	Australia
um caitlins house			Unknown
Ummerika			Unknown
under the couch			Unknown
Under your bed!			Unknown
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
Universal			Unknown
Universal City, CA	Universal City	California	United States
Up.			Unknown
Upper Gulf Coast, Texas		Texas	United States
Upper Left Coast			United States
UpState New York		New York	United States
Ur moms house ATL	Atlanta	Georgia	United States
Ur Place			Unknown
Urban Hang Suite, Milwaukee, W	Milwaukee	Wisconsin	United States
US			United States
usa			United States
USA			United States
USA (Support Arizona!)			United States
USA Canada			United States
USA- MN		Minnesotta	United States
USA not USSA			United States
USA TODAY			United States
USA, Europe			United States
USA, NY			United States
USA, the republic			United States
USA, Urrth			United States
USA,Greatest country on Earth.			United States
USA/CA			United States
USA-WI		Wisconsin	United States
ÜT: 10.25587,-67.585087		Aragua	Vietnam
ÜT: 12.979221,77.6626	Bangalore	Karnataka	India
ÜT: 14.5835,121.054894		NCR	Philippines
ÜT: 14.620538,121.017233		NCR	Philippines
ÜT: 18.960839,72.801458	Bombay	Maharashtra	India
ÜT: 18.986431,72.823769	Bombay	Maharashtra	India
ÜT: 19.053002,72.830552	Bombay	Maharashtra	India
ÜT: 19.111431,72.823609	Bombay	Maharashtra	India
ÜT: 19.432598,-99.148542		D.f.	Mexico
ÜT: 20.633207,-105.229673		Jalisco	Mexico
ÜT: 21.3069444, -157.8583333	Honolulu	Hawaii	United States
ÜT: -22.812002,148.697141	Middlemount	Queensland	Australia
ÜT: -22.957862,-43.196568		RJ	Brazil
ÜT: -22.970775,-43.181731		RJ	Brazil
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
ÜT: -26.115911,28.258501	Kempton Park		South Africa
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
ÜT: -29.814034,30.922568		KwaZulu-Natal	South Africa
ÜT: 29.958337,-90.064322	New Orleans	Louisiana	United States
ÜT: 29.9647222, -90.0705556	New Orleans	Louisiana	United States
ÜT: 29.986932,-95.348003	Houston	Texas	United States
ÜT: 3.575408,98.659303	Medan		Indonesia
ÜT: 3.59088,98.669121		North Sumatra	Indonesia
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
ÜT: 32.977929,-117.065381	Poway	California	United States
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
ÜT: 38.901584,-76.726431	Bowie	Maryland	United States
ÜT: 38.90622,-77.058475	Washington	DC	United States
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
vartopia #4real			United States
vegas	Las Vegas	Nevada	United States
vegas via pittsburgh	Las Vegas	Nevada	United States
Venezuela			Venezuela
Vengeance University			Unknown
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
Vermont, Melbourne	Melbourne	Victoria	Australia
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
Virginia		Virginia	United States
Virginia, USA		Virginia	United States
Visy Park	Princes Park	Victoria	Australia
Vitoria-Gasteiz	Vitoria-Gasteiz		Spain
Viva Bris Vegas	Brisbane	Queensland	Australia
Viva BrisVegas	Brisbane	Queensland	Australia
Vizag, India	Vizag		India
Vote 1 Aus Sex Party in Senate			Australia
WA		Western Australia	Australia
WA, USA		Washington	United States
Wageningen	Wageningen		Netherlands
Wagga Wagga	Wagga Wagga	New South Wales	Australia
Wagga Wagga	Wagga Wagga	New South Wales	Australia
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
Wall Street	New York City	New York	United States
Waltham, MA	Waltham	Massachutess	United States
Wanderlust dictated			United States
War Desk, enemy territory			United States
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