Table below. This is based on capture data from 7am Canberra time on August 24, 2010.

I’ve been occassionally collecting tweets related to the AFL from Searchtastic. I’ve blogged about that in earlier posts. If I decide to compare apples to oranges, recent tweet follower locations to tweet references by tweeter location, there are some noticable geographic differences in where people are tweeting about the team from compared to where they follow from. (This isn’t a fair comparison for a variety of reasons. First and foremost, neither represents a particularly complete data set. 2% of total followers versus unknown amount of tweets is hardly representative. I’m making it anyway, mostly to amuse myself and so I can potentially follow up later.) There are different patterns.  There are more international based tweets than international recent followers…

What would be interesting is to see if this hold up if the tweet total went up and the follower locations represented the whole population…   I just don’t know how many tweets would need to be included to even begin to provide an accurate picture.  (I’ve started logging a few #hashtags on http://twapperkeeper.com/ and hopefully I can get more Tweets archived and get some one to do a script call for me to pull in the location information of the user.)

This data isn’t really complete and I really dislike text based content analysis but the patterns seem pretty clear at the moment that the people tweeting and following the team are what is probably the ideal market for Essendon in that they can more easily monetize those fans by selling them merchandise in Australia, getting them to attend games or having them watch games on television. The raw data (only Bombers related from a larger Australian sport Excel related file) used in this post is available at Essendon.xls.

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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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I have a collection of AFL related tweets that I update semi-regularly. More often than not, I use these for geographic related analysis. My AFL tweet collection has a series of issues though that make me hesitant to always draw definitive conclusions from it. These issues 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 AFL as there are too many possible words that could reference the AFL and its clubs.
• 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 #gosaints may refer to the St. Kilda Saints or the New Orleans Saints. AFL may refer to the AFL-CIO, a union in the United States. Unless every tweet is examined, irrelevant tweets will remain in the data set.

These are major obstacles to doing any sort of content analysis for Twitter when specific subgroups are looked at. Many of these issues are ones that other researchers are likely to have too: You can’t get the complete data set, will include garbage data and may not be entirely timely.

That said, despite these limitations, I still wanted to know what are the most popular #hashtags for AFL related tweets and what type of hashtags are likely to be ReTweeted.

To get my dataset of Tweets, first I went to Searchtastic, ran a series of searches over the course of 6 weeks, and exported those results to Excel. I noted the keyword I searched for and the team that keyword is related to. The keyword searches that were run on Searchtastic during this period included: #afl, #aflbluestigers, #aflcatslions, #afldemonsswans, #afldogscats, #afldogsfreo, #afldogspower, #afleaglesblues, #aflfooty, #aflhawksdemons, #aflkangasbombers, #aflkangasfreo, #AFLPAQUIZ, #aflpiestigers, #aflpowercrows, #aflpowereagles, #aflsaintshawks, #aflswanshawks , #brisbanelions, #dreamteam, #footy, #fremantle, #freo, #freo AFL, #goblues, #gobombers, #Gocats, #gocrows, #gopies, #gosaints, #goswans, #NMFC, #northmelbourne, #sackermanis, #supercoach, #sydneyswans, #teamgws, #thekennel, #wafl, #westcoast afl, ackermanis sacked, ACT4GWS, Adelaide Crows, Adelaide_FC, afl Bombers, AFL GWS, AFL Magpies, afl pies, AFL Tony Lockett, Aker Bulldogs, Akermanis, akermanis dogs, akermanis gillard, Australian rules, blues afl, Brad Johnson, Brad Johnson AFL, Brady Rawlings, brisbane afl, Brisbane Lions, Brownlow Medal, Cameron Mooney cats, Carlton Blues, Chris Judd Carlton, Collingwood, collingwood AFL, Collingwood Magpies, crows afl, Damien Hardwick, DemonsHQ, Dockers AFL, eagles afl, eagles perth, Eric Mackenzie , Essendon afl, Essendon Bombers, etihad kilda, folau afl, folau israel, Fremantle Dockers, fremantle ruckman, GCFC, geelong cats, Gold Coast Suns, harry_o, Hawthorn Hawks, HawthornFC, Jason Akermanis, Jeff Kennett, Judd Blues, kilda, kilda AFL, kilda riewoldt, lions afl, Mark Harvey, mcg kilda, Melbourne Demons, milburn cats, Nathan van Berlo Crows, North Melbourne Kangaroos, PAFC, Peter Szental, Port Adelaide Power, portadelaidefc, Richmond Football, Richmond Tigers, Rodney Eade dogs, roos afl, sackermanis, spoon eagles, spoon lions, spoon tigers, spoon wce, swans afl, swans footy, swans ruckman, Sydney Swans, tigers afl, Travis Johnstone, Trent Cotchin, WCE AFL, West Coast Eagles, and Western Bulldogs. These keywords represent all teams and often include multiple keywords for them.

The next step was to remove all duplicate tweets. As some tweets contain multiple keywords and some searches were run more than once over that 6-week time period, that possibility existed. To do this, three columns were copy and pasted to a new worksheet: Username, Tweet and In Reply To. On Excel, I did this using Filter -> Advanced Filter -> Unique records only. This process took the total tweets from 8,523 to 5,427.

The third step was to identify all the hash tags used in this data set. 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. The completed list was 2,833 total hashtags used. Of these, 556 were unique. The following table counts their total usage:

The next step was to count the total @ replies and hashtag uses. To do this, all the unique tweets were copied to a new worksheet. A filter was created to list all tweets that did not include an @ sign. These tweets were deleted. This brought the total tweets from 5,427 to 1,844. The next step was to create a filter to show all tweets that did not include a #. These tweets were then deleted. This brought the total Tweets down to 832. The next step was to look remove tweets that did not contain “RT @”, “RT:@”, “retweeting @”, “retweet @”, “via @”, “thx @”, “HT @”, or “r @”. This leaves 458 tweets that are retweets that contain hash tags.

The same process was conducted to count the hash tags that was used for all hash tags and RT @. The following list was created of people who were the most ReTweeted (or mentioned in the ReTweeted tweet) where the tweets contained #hashtags and the #hashtag totals for ReTweets. These were then totaled and counted, resulting in the following table:

Based on this sample, (because the whole of the population of AFL tweets is not available and cannot the total cannot be counted to determine the representative value of the sample) the most RTed #hashtag in a Tweet related to the AFL is #gosaints. #dreamteam comes in second. #gocats comes in third. #afl comes in fourth.

The #gosaints tag is popular and the team’s official account has encouraged its use. More recently, New Orleans Saints fans have used it as the NFL pre-season has started. #dreamteam is one of the two most popular AFL fantasy leagues and those fantasy leagues are big into fostering community. Beyond these top four, the most popular tags tend to involve specific match ups. If all tags related to match ups had been included around the time that the games took place, it is highly possible more would be on that list.

In terms of who gets RTed and mentioned in ReTweets, official accounts dominate: The AFL, Carlton, St. Kilda, Hawthorn, the Bombers, Geelong and the Swans. The first non-team account to appear is P. Diddy and he is followed by a number of influential Australian social media people. The next person / non-official AFL account to appear is Julia Gillard. AFL players do not appear to be ReTweeted en masse when compared to their club sides. This isn’t to say that people don’t actively read and follow them. If looking at ReTweets without #hashtags, @harry_o would appear on top with 50 mentions. It is probable that clubs are using #hashtags and players are not; there could be a digital cultural awareness divide between the two.

A copy of the data and some of my step by step process making it workable can be found at RTAFL.xls. I find the whole thing really interesting. If anyone has any tools to make dataming easier or has a suggestion for additional research in this area, let me know as I’d love to hear it.

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This post is about the Yahoo!Group’s Essendon Bombers community.The Essendon Bombers directory on Yahoo!Groups has twenty-one mailing lists.  Of these, fifteen are actually about the Essendon Bombers.  The lists are ultimateessendonbombersclub, bombersrule, essendonsofficialyah00club, marksessendonfansroom, essendonunknownandforgotten, bombers4eva, gobombers, thewindyhillbombers, bombersofficialyah00club, the-bombers, babybombers, essendonbombers, ihopeessendonchoke, thebomberfanclub, and essendon.  Of these, three have never had any real activity (essendonunknownandforgotten, bombersofficial, and the-bombers), two have had one post (essendonsofficialyah00club, essendon) and six others have had fewer than ten posts (bombersrule, marksessendonfansroom,   gobombers, thewindyhillbombers, ihopeessendonchoke, thebomberfanclub).  Two were created in 1999, eight were created in 2000, four were created in 2001 and one was created in 2007.

I  added real posting from the lists together and created the following chart:

This community was active between June 1999 and October 2002.  After that, the community basically died off with almost no posting.  This period between 1999 and 2001 was one where the team had performed rather well.   In 2000, the team won 20 games in a row.  After that, the team did not perform as well.  Compared to the other teams looked at, this pattern is a bit weird because of the drop off after 2002.  The total volume, both in terms of total mailing lists and total posts, is also different.  It makes that drop off all the more fascinating.  The drop off in activity may possibly relate to other factors, such as the small size of the community on LiveJournal clones that were most active in the period immediately as this community went inactive.  It will be interesting to compare the community creation dates on bebo and LiveJournal, to see if the population may have shifted towards those sites.

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