Anyway, the basic math for the Swans followers statistics:

Beyond that, what I was most interested in was the location of Sydney Swans fans. Would they be centered around Sydney? I put the locations on a map and this is what it looks like… (Yes, huge fan base around Sydney. At the same time, large fanbase around Melbourne and scattered fans/followers around the globe.)

View full map

The raw data set used for this can be found at TwitterSwans.xls.

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The official account for the Melbourne Storm is MelbStormRLC. They have 1,757 and we got information from all the accounts following it. For the major categories involving numbers, I got the mean, median and mode:

The numbers are very, very low when compared to AFL based St. Kilda Saints. The Saints have around 140 people who have 10,000+ followers and 15 people with 100,000+ followers and that’s with only half their followers counted. In contrast, the Melbourne Storm have no one 10,000+ followers. They also have no one who follows that many people. On the opposite end of the ladder, there are 52 (or 2%) people who follow only the Storm or the Storm and one other person.

Overall these stats suggest that people who follow the Melbourne Storm are not particularly active on Twitter: They don’t follow that many people, they aren’t followed by people, they update infrequently and they don’t appear on many lists. What these stats don’t tell is how often these people check Twitter. Some people might check Twitter often but not update and follow many people. (Different people use Twitter differently. It would be really interesting to see a survey that asked people how often they check Twitter and why they check Twitter.)

Beyond that, 1,308 people have their timezone information available in their profiles. (It might actually be more and this could be a problem in our data collection… but let’s pretend that this is representative of the whole group despite evidence to support that.) The following is a count by timezone:

Unsurprisingly, given that the team is based in Melbourne, that timezone is the most popular, accounting for 44% of the team’s followers. Sydney time, the area where NRL is dominant, accounts for 20%. I’m not certain how to explain the popularity of the Melbourne Storm in Hawaiian time. Brisbane makes sense as again, the area is an NRL stronghold.

In profile descriptions, 36 people mention the word Storm and 103 mention Melbourne. That seems like a fairly significant amount of people expressing team loyalty and seems comparable to the Saints.

Four people list a language code of ES and three people list FR. That’s less diversity than St. Kilda in terms of language. Still, the French ones seem more explainable than the ones for St. Kilda because rugby is played in France and players sometimes go there to play for French teams. It can help to develop an audience in Australia for the team.

The Melbourne Storm don’t have as many followers as the more popular AFL teams but they do have more followers than the Canberra Raiders. The controversy early in the season helped to boost their numbers and they’ve been much more interactive of late. … Or at least, they’ve relied less on TwitterFeed for content. That was a huge problem early on in the season. Beyond that, these numbers look realistic: They appear to be getting an audience of Melbourne based and Australian NRL fans following them. Most of the people following them update occassionally and have few enough followers that they will see the content that the Storm update with. That’s probably the ideal that people should be aiming for. Yeah, people with 100,000 followers are awesome, especially if you can get a ReTweet but they’ll never see your tweet and in the case of St. Kilda, a lot of those people with massive follower counts are just not in their ideal demographic.

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Basic math out of the way, the following are the numbers for Richmond followers based on that sample of 1,099 followers:

These numbers are a lot higher than the Melbourne Storm, though not as high as St. Kilda. They average 479 more followers, 161 more friends and on average are included on 10 more lists. The big difference is that Richmond Tiger fans appear to update a lot less than Storm followers with an average of 170 less status dates and a median of 25 less status updates.

The Richmond Tigers only really have two power users with more than 10,000 plus followers. They are: michaelyardney and HootSuite. Both are also the only two people who follow more than 10,000+ people on Twitter. (Remember: This sample is less than half the complete set. There may be more.) On the other side, there are 53 people who follow only the Tigers or the Tigers and one other account.

670 people appear on zero lists. 116 people appear on one list. At the high end, ignoring HootSuite, fourteen accounts are listed on 100+ lists and four accounts that follow Richmond are listed on 200+ lists. The 100+ list accounts are: heraldsunscoop, NovaFM, PAFC, Geelong_FC, outfandishfans, superfooty, stkildafc, ryanknapp, SportsGeekHQ, haydencooper, michaelyardney, Adelaide_FC, Collingwood_FC, sammutimer . A lot of these are AFL related. (AFL clubs and media follow each other.)

156 people have never updated their Twitter account. That represents 14% of all Richmond followers. 443 people have updated 10 or less times. That represents 40% of all Richmond followers. That is a huge number of people who are just not updating frequently. It may suggest that people joined Twitter, followed their favorite team and then never bother to check again. (Or again, it may not. Some people may check Twitter with out updating. I know a few cases.) 8 of Richmond’s followers have updated their status 10,000+ times. These really active followers are: THETUBEtv, FtLauderDaniel, D3wizl, _Jessica_Maree, asmtegan, dottedwithearts, aflfeeds, dzufauzan. (In that select group of 10,000+ super updaters, they average 624 followers and 745 people they follow.)

Beyond these basic numbers, 720 people have visible time zones. Over half of these followers are in Melbourne time:

I still need an explanation as to why there are so many people in Hawaii who follow Australian sport teams…  (Turns out a lot of Melbourne people are listing their timezone as Hawaiian…) Sydney appears third, Adelaide 4th, Brisbane 5th, and Perth 6th. This seems to match a bit with my understanding of the popularity of the AFL around Australia.

As for languages, 1 for DE, 2 for ES, 1 for FR (who lists his timezone as Greenland) and 1 for JA. This reflects a little less diversity than the Storm and the Saints. It may possibly suggest that the Tigers are less prone to getting spam followers than the other teams if one assumes that non-English language codes imply spam followers.

Either way, the Tigers seem much closer to the Storm than the Saints in terms of follower patterns. The big issue for me as some one looking at it for the good of the team is that they appear to have a really high inactive base that may suggest people aren’t continuing to read their message.

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One stat that interested me is posting client. What client are people using to post about the #afl? I’ll give you a clue to the most popular: It is an automated one that involves no human posting.

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The average number of followers a St. Kilda follower has based on that sample? 3169. Average number of people they are following? 3233. Those numbers seems absurdly highly. That’s because you have 142 of them with 10,000+ followers. You’ve also got 15 with 100,000+ followers: MagicSauceMedia, Dillanos_VP, SanMarcoCoffee, mauricefreedman, wishnie, MarcDeCaria, darrenwilliger, dollars5, socialmediamind, WeBlogtheWorld, PleasureEllis, pramitjnathan, rleseberg, Wakooz_RSS, DesignerDepot, clatko. If you remove those with 100,000+ followers, you get the average number of people a St. Kilda follower is following down to 2258. If you remove the people who have 10,000+ followers, you get the average number of number of followers a St. Kilda follower has down to 247. That number is much more realistic.

The number of people following is one of those problem parts of my data set. (We’re still refining this data collecting. This will get more accurate with time.) We only have information for 2,293 as compared to 2,397. Of those, 13 are following more than 100,000 people. (They’ll never see St. Kilda updates. Can’t follow 100,000 people.) If those 13 are taken out, the average number of people that a St. Kilda follower is following is 2388. If we take out the people who are following more than 10,000 people, the average number of people they are following is 350. This number appears more realistic. Oh and those followers? 93 of them are following only the St. Kilda Saints. 51 of them are just following the Saints plus one other account. The median number of people that they follow is 81.

Most St. Kilda fans aren’t following and being follow by all that many people. There are a few people who heavily skew the average.

The average number of status updates for a St. Kilda follower? 1044. 49 people have updated more than 10,000 times. If we take them out, we get an average number of updates of 539. If we get rid of the 460 people who have updated 0 or 1 time and those with 10,000+, the average number of updates is 671. On the whole though, when you have 460 people who have updated 0 or 1 time, that is a huge population of people not updating on Twitter…

One of the other stats that were pulled involved getting the language code. (What language do you view Twitter in.) Most of these are English with a few exceptions: 3 DE, 9 ES, 1 FR, 3 JP. Most of St. Kilda’s fans opt for English as their native language.

In their profile descriptions, 23 mention St. Kilda and 32 mention the Saints. A few of the latter obviously are confused bots or people who can’t tell the St. Kilda Saints apart from the New Orleans Saints.

If I just filter the location down to require that they mention Kilda, there are only four. That isn’t many people. When I get the location tool fixed, I should be able to get a better idea of what is going on with where they are from. (I have an If location field says this, then that means they live in City, State, Country. This only ran for about 100 people before it broke. Otherwise, I would discuss this more.)

I’ll do another similar analysis when I get more data. The tool needs to be refined but the insights I begin to see what followers look like are fascinating.

A copy of the raw data referenced in this post can be found here: StKilda.xls.

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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 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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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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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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• 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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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: