Ozzie Sport

I wanted to feel a bit productive this afternoon so I thought I would go back and look at one of the easily measurable populations that I have. In this case, I chose Dreamwidth Studios. Was there a population shift since June when I last recorded how many people listed a team as an interest? Nope. Data below.

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• For Perth based teams on bebo, bloggger, 43things, #wce most popular, #freo is 2, #waforce is 3, PerthGlory is 4, PerthHeat is 5 #
• Gridiron Australia is based in the ACT! E-mailed them to ask if we could meet and discuss developing fan base for my research. *fingers xed* #
• Off task. Totally off task. Not sure what on task is. Should do that. #
• My new fave hobby in sports stores? Talking to workers about official club merchandise and frustrations as a consumer. #
• Finished reading "The Rale Rasic Story". Really interesting read about Australian soccer. #
• Is there a tool to batch convert shortened urls to longer versions? I have a list of 1000 bit.ly urls I want lengthened. :/ #
• Looks like #afl fans on Twitter are not big into citing Wikipedia… #
• Sample of 665 urls (from 2,942 urls from 6313 unique AFL tweets), most popular domains? twitpic.com, afl.com.au, facebook.com, youtube.com #
• Found a URL extender this morning. Why couldn't I find it last night? #
• Full list of urls referenced in AFL tweets: Most popular domains? afl.com.au, foxsports.com.au, heraldsun.com.au, au.sport.yahoo.com #
• These results are very different than #afl related RTs with #hashtags . #
• What domains in #afl related tweets get the most RT @ ReTweets? : bit.ly/cJfNBY #
• Twitter #hashtags and the Australian election : bit.ly/aJdX1M #ausvotes #
• One last #ausvotes Twitter election related map : Labor vs. Liberal : bit.ly/bU8r8B Where do #libs and #lab dominate on Twitter? #
• #ausvotes Tweet totals by electorates : bit.ly/dkaiPQ #
• Post #ausvotes analysis and commentary : bit.ly/93BJxx #
• Give me a few days and I will get back to AFL analysis. Maybe. Need to avoid Twitter content analysis. It is useless. #

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The last post? It leads me to believe that you cannot use tweet volume by electorate to predict the outcome of that electorate’s voting. In most cases, the sample sizes were just too small: 1 to 10 tweets. There might have been more tweets coming out from that electorate but they weren’t using party names, acronyms or the names of the major party leaders. An already small sample of say 50 tweets from an electorate could be whittled down to 5. (I could go back and redo that, try to get more tweets, include post election tweets and see if the results changed… but I’m not certain the point. If some one gives me an incentive to do that though, it will be done.) Twitter doesn’t appear to have any meaningful predictive value on that level… or even on a broader level (where Labor was mentioned almost twice as often as the Liberals. The best suggestion could be that the overall trend is that Labor and Green supporters engage in the usage of social media more than their Liberal, National and Family First counterparts.)

That said, I still want to analyze these patterns. I generally avoid content analysis but this is interesting and some what relevant to an important topic. (Will Tony Abbott win and adversely effect my desire to stay in the country post graduation? How will the elections impact the country’s spending and the value of the USD, which I rely on to pay my bills in Australia?) I asked an acquaintance what analysis she would like and she suggested sentiment analysis post election results. (Are people on Twitter happy with the results?) I’m not a fan of sentiment analysis for a variety of reasons… It isn’t easy to do and is rarely accurate. The easiest way to do that would probably involve letting people judge the attitude of Twitter folks towards the election based on the most popular #ausvotes related hashtags.

To do this, I went to searchtastic and searched and searched like I did before (using the following searches: #ausvotes, #asuvotes, gillard, abbott, greens house, greens senate, #sexparty, #familyfirst, #nbn, #campaignbender, ranga julia, #vote9, #hungparliament, hung oz, #auslabor, Australian sex party, #9votes, #laborfail, #myliberal, Joe Hockey, tony julia, Maxine McKew, Wyatt Roy, wilson tuckey, Stephen Fielding, Steven Conroy, bob brown) . I found over 10,000 total tweets using these searches. I then removed all tweets that were posted before Aug 21 2010, 0:00 UTC. This time would be 21 August 2010, 10:00 AM Canberra time. People had started voting by then and most of the election vote getting had been done. That done, I had 7,985 total tweets. The next step was to remove all duplicate tweets. The tweet total is 3,372, which looks like a pretty decent sample size.

After that, I extracted all the #hashtags from these tweets. These 3,372 tweets had 4,354 #hashtags used in them. There were 420 unique #hashtags. The table below is a count of the most popular #hashtags used after Aug 21 2010, 0:00 UTC where the tag was used at least twice:

Is there any sentiment being expressed by people interested in the Australian elections that you could take away from this? Probably not. The extent to which I’d say any attitude is being expressed is that people do not want Abbott to come out on top and that they blame Labor for this failure of having a clear victor and functional government. It could also possibly be read that people are concerned about the impact of the elections on Internet censorship and infrastructure in Australia. (Though #nocleanfeed could refer to the Wikileaks story. People often use that #hashtag to discuss the site.) Marriage equality and civil rights are viewed as being a post election concern. And I suppose you could also conclude that Australians are keeping their sense of humor about themselves with their use of various puns and sexual references.

Rather than do another post, I’ve decided to tack on a list of URLs that were mentioned. From that same list of 3,372 tweets, there were 516 total urls included in the tweet. 297 of them were “unique.” (I haven’t lengthened all of the URLs so there may be a few repeats using url shorteners that I can’t easily lengthen.) The following URLs were the ones that were all mentioned five or more times:

It is an interesting selection of links: YouTube, Facebook, ABC news, Yahoo News, Labor’s website, TwitPic, and YFrog (a picture). The only really official content is that from Labor. Despite the #hashtag popularity of the Sex Party, people weren’t linking to them. People also weren’t linking to Get!Up content on their site. They weren’t linking to other official party content. The message in this case is clearly in control of voters and the media, not the parties.

Anyone have any more suggestions for what to write about in terms of the Australian elections before I go back to sport?

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The Twitter collection I had and I’ve been posting about? I spent an hour or two getting Tweets. I stopped getting Tweets around the time the polls opened. (See this post and this for a more detailed methodology used for data gathering.) After that, I sorted the Tweets out to get rid of the duplicate Tweets. Then I get rid of the obviously not about Australian politics tweets based on the following keywords: glennbeck, #palin, #gop, #teaparty. The next step was to figure out from the location field where a tweet originated from. If the Tweet was not from Australia and I couldn’t figure out which city in Australia it was from, it was discarded from counting. This left 17,802 tweets total. After that, I sorted tweets based on party: Labor, Liberals/Nationals, Greens, Sex Party, Family First. For each party, I used that word, the name of the leader, prominent members of the party and the acronym they go by to get a more complete list of tweets. (Hockey = Joe Hockey = Liberal. Julia = Julia Gillard = Labor. Etc.) Then I tallied up the total of tweets from each city. I sorted the cities by electorate… and I got the following table. If you don’t see an electorate, no Tweets were identified as having come from it.

Some of the results surprised me as I thought that the seats leaned Liberal or Labor and the tweets mentioned the other party more. After the results are in, I’m going to have to revisit this to see if there is any correlation between tweet volume by party in an electorate and the results for that electorate.

If anyone could make this into a map, that would be extremely awesome. A copy of the Excel file used to build these totals can be found here. Warning: it is 13 meg. If you want to see all the raw tweets and the keywords they were found under, AusVotes.csv. Warning: this file is 14 meg.

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Before I went to bed, I wanted one last Australian related election post. This post draws on the data and methodology from my other posts and maps.

At around 6pm, I did four additional keyword searches on Searchtastic beyond the ones in my most recent post. These were: #ausvotes, #donttrustabbott, #nocleanfeeds, #electionwire. I added them to my complete list of Tweets, then filtered the tweets based on username, location and tweet to remove duplicate tweets. This brought the total of 60,0084 Tweets down to 54,962. I then added three columns: City, State, Country. For the next few hours, I endeavored to fill in as many city fields as I could. The focus was on identifying Australian cities. Thus, for countries not Australia, I just listed the country or unknown. This was so I knew to ignore those. At 8pm, I stopped completing the fields. I just don’t have time to label everything…

Of those 54,962 tweets, some form of identification was completed for 34,213 tweets or 62% of all tweets. (Not a bad sample size from the whole.) Of these tweets, 19,165 came from Australia. Of these, 15,629 have Australian cities identified for the location of the Tweet. That I can play with.

The next step is to identify noise of Liberal, Labor and the Greens by city. The following CONTAINS filters were created on Excel to find tweets to get location data: Labor OR Gillard, Abbott OR Liberal, Brown or Greens. There were 1207, 977 and 302 tweet locations respectively. After this, the total number of Tweets per city for those terms was counted. There were 61 cities with Labor tweets, 62 cities with Liberal tweets, 38 cities with Green tweets.

After this was done, city locations were geocoded for mapping. I like to use BatchGeo. This gives me Latitude and Longitude which make the dots on the map locate more accurately. The tables were then ported over to Geocommons Maker and the following map was generated.

View full map

In table form, this map looks like:

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Can you learn about an election by its Twitter #hashtags ? I’m curious to see if the #hashtag use on Twitter matches with what people see as the major issues that the newspapers and news media covered. (I’m also interested in seeing what #hashtags they ReTweeted but that will likely have to wait for another post.) There are a couple of challenges when doing something like this… and be wary of anyone not spelling out their methodology because if you don’t know their methods, you can’t fairly evaluate their results and subsequent conclusions. Social media research is very much creative research that takes place in the moment. While you may not ever be able to get the exact same results, when you repeat a person’s methodology, deviations should be explainable. Anyway, the challenges and assumptions with doing a #hashtag analysis of the Australian elections include:

• Incomplete tweet set for keywords: I only get what is available from Searchtastic.
• Incomplete tweet set based on keyword limitations: Even supposing I could get all the tweets related to a keyword, I can’t find every reference to the Australian elections as there are too many possible words that could reference the elections and all the candidates running nation wide.
• Incomplete tweet set because of time: Some keywords were searched for earlier and some later. Not all keywords were looked at over the same period.
• Irrelevant tweets: Keywords like #greens may refer to the Green Party in the United States. Liberals may refer to American or British liberals. Unless every tweet is examined, irrelevant tweets will remain in the data set.

To offset some of these problems, a large data set was acquired. (Because a lot of people are tweeting about the elections. I see way more election tweets than footy tweets.) These tweets were acquired using Searchtastic and the following keyword set: #Abbott, #alp, #alp vote, #arbib, #aus2010, #AusLabor, #ausvotes, #ausvotes abbott, #ausvotes gillard, #boatphone, #GILLARDTINED, #greens, #laborfail, #masterchef abbott, #masterchef debate, #masterchef election, #masterchef gillard, #myliberal, #nocleanfeed , #NPC, #ozvote, #qanda, #spill, #tcot, #workchoices, @AustralianLabor, @LiberalAus, @TonyAbbottMHR , abbott math, Abbott-Gillard, asylum boat, australia vote, canberra election, debate abbott, debate gillard, debate winner, Following: @JuliaGillard, Following: @SenatorBobBrown, Following: @TonyAbbottMHR, Gillard, greens brown, Greens Canberra, greens election, Gruen Report, Gruen transfer, hockey liberals, immigration australia, Julia Gillard, JuliaGillard, Krudd Labor, KRudd Liberals, Labor darwin, labor leaks, Labor Liberals, Labor Tasmania, Liberals Tasmania, libs abbott, libs darwin, Libs Howard, marginal electorate, marginal seats, NSWLabor, perth vote, preferences vote, Rudd Liberals, Sex Party, stimulus liberals, sydney elections, Tony Abbott, tony boat phone, tonyabbottmhr, Truss LNP, Truss nationals, Warren Truss, Work Choices Australia, WorkChoices .

These keywords represent the major parties and candidates, some of the major issues, #hashtags that I saw on my Twitter feed, and different geographic areas around the country. Searches were run between July 19 and August 19. A total of 57,977 tweets were collected. The elections were called on July 17. To make sure that the collection of tweets pertain to the elections, all tweets made before July 1 were removed from the data set. (Methodology: Sort tweets by date on Excel. Remove those tweets not between those dates.) By then, everyone knew the elections had to be called and conversation regarding them had started. This takes the total tweets in the data set down to 21,071. That’s still a fairly large collection of tweets to work from. The next step is to remove duplicate tweets from the data set. (On Excel, Data -> Filter -> Advanced Filter -> Unique records only.) This brings the total tweets down to 18,462. That’s still a lot of tweets.

The next step is to extract #hashtags. To do this, I copy and pasted all the tweets to Notepad. I ran a find and replace for [space]# and replaced with [tab]#. I copy and pasted these back, removed all cells that did not start with a #. After this was done, the data set was copy and pasted back to Notepad. Another find and replace was done, this time, [space] was replaced with [tab]. This was pasted back to excel and all cells that did not start with # were deleted. When this was done, 33,857 total hashtags were found. Symbols like , ! . – ? were removed from those #hashtags. This was done to make that #labor. and #labor were treated the same for counting purposes.

A list of unique hashtags was then attained of which there were 2,678. The following table includes all #hashtags that appeared 250 or more times on the list:

Looking at this list, there are some phrases that are likely not Australian or not uniquely Australian. This includes #tcot, which stands for Top Conservatives On Twitter. The term was amongst those searched for because it appeared in a few tweets that also included the #ausvotes #hashtag. A google search for #tcot #ausvotes only brings up 8,120, which further supports the idea that this isn’t really an Australian election term. #tlot was not deliberately searched for in terms of trying to include it. If you put #tlot #ausvotes into a google search, you get 244,000 results which suggests heavy Australian usage. You could probably remove #teaparty, #GOP, #Libertarian, #Israel, #Twisters, #USA, #Obama, #flotilla, #Gaza, #912, #oilspill, #US, #jews, #BP, #antisemitism, #oil, #Muslim, #ireland, #UK, #Iran, #Palin, #Europe, #dnc, #cdnpoli, and #iranelection. They are unlikely to do with the Australian elections.

If that’s agreed upon, then it looks like top issues based on #hashtags … the internet and its openness? It doesn’t look like there was any large scale usage of #hashtags around issues. Instead, it appears that #hashtags were used to label tweets that discussed the election, were used to discuss specific candidates and to discuss specific parties. Issue based discussion may have been secondary to Twitter discussion.

And if that’s true, and going further with that idea, it could validate the messaging used by Labor and the Liberals to largely mount attacks on each other. People on Twitter are heavily engaged in discussing politics but not the issues. It may also justify the work of GetUp!, which strives to bring attention to specific issues in Australia.

If you want access to the Excel file with all the tweets, please comment or send me an e-mail. The file is about 24meg so I didn’t upload it.

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This post is a follow up to What #afl related hashtags get the most RT @ ReTweets? and Want to be Retweeted? Add URLs to Your Tweets!. This post looks at the what domains get the most retweets in tweets related to the AFL. It borrows the methodology (and assumptions) from the previous post with two major differences. First, the tweet collection had 10,412 total tweets instead of 8,523 at the start. (More recent tweets and more tweets.) Second, a new tool was needed to lengthen short urls. The tool that was used was Long URL please, a FireFox extension. Short links were copy pasted from Excel to a draft document, urls were lengthened and then pasted back. (When URLs were not lengthened, they were manually visited to get the real url.)

That out of the way, there 10,412 total tweets that could be examined. The list of keywords used can be found at using my raw data. Duplicate Tweets were removed bringing the total to 6,313 unique tweets. Tweets that did not include a url were removed. This brought the Tweet total down to 2,892 tweets. (About 46% of AFL related Tweets include a URL. This compares to 52% of AFL related Tweets with #hashtags.) There were 2,941 urls in these Tweets, with 2,582 unique urls in these Tweets. Of these, there were 623 unique domains. For all AFL related Tweets with urls, the following domains were the most popular:

Of the Tweets with urls, only 342 of them were ReTweets. That means about 5% of AFL related ReTweets contain domains. (Compare that to ReTweets containing #hashtags: 8%.) There were 150 domains mentioned Amongst those, the following domains were the most popular:

The domains that are ReTweeted are interesting. It is really easy to link spam on Twitter and some people and organizations do. If you look at some accounts, they post view TwitterFeed. Beyond that, you can tell when looking at a URL list that automated link posting is happening because the account may be using a URL shortener that isn’t as popular and the URLs tend to follow a sequence. The AFL and various news services put out URLs and they just aren’t getting ReTweeted at the same rate that they are getting posted. What is getting ReTweeted? People’s pictures and club related links.

Looking at both the domain list and the Domain ReTweet list, one thing that stands out for me is the lack of Wikipedia links. Wikipedia ranks really high on Google and other search engines. The AFL related content is highly ranked. The AFL articles are often really, really, really good and they are updated frequently. They can be really useful when looking for historical information. Thus, I’m surprised that there were only 3 references to Wikipedia. What gives? Why isn’t Wikipedia being cited more?

Moving on, 488 accounts were mentioned in the 343 ReTweets with URLs. The following table gives an idea as to who the most popular mentions were:

That Essendon and Sydney were amongst the most mentioned doesn’t come as a surprise: Their domains were highly ranked. What is a bit surprising that the Blues aren’t highly ranked; they ranked highly for mentions with #hashtags. St. Kilda ranked highly on both lists. I half want to attribute this to the idea that their audience may be more Twitter savvy than other clubs’s fans. I’d have thought players might have been higher on this list but they really don’t appear to be a factor. This may suggest that players are not Tweeting urls or that content isn’t what their fans are interested in.

The lesson of these two posts is that if you want your AFL related tweets reposted, be partisan in your support and link to official club content that the news services haven’t yet written about yet.

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

Methodology

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

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

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

Online target analysis of behavior and psychographics

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

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

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

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

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

Predictive analysis

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

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

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

References

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

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

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

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

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I feel guilty as I’m kind of posting as I do with out any particular pattern. Anyway, I looked at Google.com.au and friendster (both of which have a load of garbage unrelated results) and recorded the totals for number of results for each team. I also did that bebo, care2 and blogger. (I also did ecademy and blackplanet but results were 0 the whole way through.) I’ll see if I feel like exploring and recording the size of the Perth sport fandom some more. The following table includes the results so far. If you have methodology questions, let me know.

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I haven’t done a real data dump day in a while and today appears to be the day. I want a distraction and trying to figure out the most popular sport teams in the Perth area fits the bill. The first is a list of goals for each time on 43things.com.

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