Archive for category Other interests

An Australian sport wiki

Posted by Laura on Monday, 11 October, 2010

I’m currently toying around with the idea of creating a large Australian sport wiki. I’ve had a lot of data for a while to allow me to do this. Some of the data I have includes:

  • A list of Australian sport teams.  List is about 3,000 teams long.  It often contains some other information like when teams were founded, team colors, etc.  This can be combined with a list of Australian sport teams on Twitter and Facebook.
  • A list of Australian sport venues.  This can be merged with a list of Australian sport venues on Gowalla and Foursquare.
  • A list of Australian athletes on Twitter and Facebook.  This is probably about 100 large.
  • A list of Australian sport organizations on Twitter and Facebook.  This is probably about 50 large.
  • A list of Australian sport fans on Twitter, Facebook, LiveJournal and its clones, bebo, orkut, Wikipedia.  This list is probably close to about 20,000 long.

This data could easily be supplemented with the following information that would be easily mineable:

  • A list of AFL, NRL and other Australian professional athletes sorted by team.
  • A list of other Australian athletes.
  • A list of Australian sport organizations.
  • A list of Australian sport journalists and broadcasters.
  • A list of Australian sport websites.

This list could be further supplemented by:

  • Importing Australian sport pictures from Flickr.
  • Importing Australian sport videos from YouTube.

The quality of the wiki wouldn’t necessarily be all that high.  Most of the initial content would be stub.  Still, if I had a bot programmer and I was willing to devote a month to the project, it would be reasonably comprehensive in terms of initial content for others to use to build from.  It would possibly help in terms of developing a large idea of the scope of sport in Australia by giving an idea as to the size of the fan community, the depth of the interest in various sports, the number of teams, when those teams were created and folded, where sports are more popular than others.  (Though that all depends on the data mining that goes into the initial content.)  What keeps me from doing this is the fact that I feel like it might be a time sink of a month to create this content and I’m not sure if the time sink is worth the effort.  Basically, I need external motivation.

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Twitter: A Solution to the Follow Spammers

Posted by Laura on Tuesday, 5 October, 2010

I’m having another period of annoyance with Twitter. I really feel like I should probably turn off alerts for followers again because right now? I’m pretty much putting people on a spammer list if they have 2,000 people they already follow. I’m also sending out cranky DMs blasting people for doing this sort of following.

For the past two months, I’ve spent a lot of time looking at Twitter. I’ve looked at follower counts. I’ve looked at follower geographic patterns. I’ve looked at people’s descriptions. I’ve looked at people’s geographic locations. The point of this is often to determine the geographic location of Australian sport fandom. I’ve read a fair bit on technology blogs about Twitter to help further my own understanding of Twitter to help me with intended mini-literature review in my Twitter chapter of my dissertation. I’ve basically been ODing on Twitter. There is a lot of interesting stuff out there.

But as a user? I’m getting pretty cranky. Seriously cranky. Every day, it feels like I’m getting 2 to 10 follows (across about 3 different accounts) from people who I don’t know, who are not geographically close to where I’m writing about, who don’t appear interested in professional sports, who have low interaction rates, who have 2,000+ people they follow. As part of my research, I constantly ask: What is the ROI for a team on Twitter in terms of where their audience is located? How can they best leverage their network? What can they provide for their fans to induce them to follow them? How can their fans help them? As a user, I can’t see how the people like I describe who follow me gain any benefit from that. (They can’t read me. I can barely keep up with 350. I function more or less because Americans get neglected as they post while I sleep.) (In one case, I got followed and unfollowed by about 5 times by the same user with 4,000 followers. ) As a user, I can’t see what they offer me. They rarely bother to explain.

And this is killing my desire to stay on Twitter. Seriously killing my desire to stay on Twitter. I just can’t. There are people I want and need to keep track of on Twitter for professional reasons. (The personal ones are almost exclusively on Facebook these days. On that level, I don’t feel the need to stay.) If you’re not active on Twitter and you cover social media, people sometimes doubt your legitimacy because you’re not using the product you’re discussing.

What I’d really like is for Twitter to make the following reforms:

1. Add a field for follow philosophy. It can be selecting from a list. It can be freeform writing. This way, when people follow others, they can see if they have a mutual philosophy. “I follow back people everyone.” “I follow friends, family and professional acquaintances.” “I follow celebrities.” “I follow only people with less than 1,000 followers.”
2. Allow people to block people with certain follow totals unless you follow them first. (I want to block anyone with 1,500 people they follow from following me first. If you want to follow me, interact with me first. Otherwise, add me to a list.) This way, spam following by power users is cut back.

The two following methods would help to kill off the Twitter spam following (and yes, your unwanted e-mail notification that you followed me to never read me is spam. It is unwanted and unsolicited and you didn’t indicate any mutual interest.) and help prevent my own fatigue. I use and prefer Facebook more than Twitter precisely because I’m not inundated with unwanted announcements like that.

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Sport culture shock: America vs. Australia

Posted by Laura on Friday, 10 September, 2010
Footlocker Belconnen: This isn't the USA

This is the Footlocker in Westfield Belconnen. Notice the lack of Australian sport team merchandise and the presence of American sport merchandise.

I’m embarrassed to admit it but I think, after four and a half months in Australia, I’ve finally developed a bit of sport culture shock.   Before hand, I could enjoy the differences between American and Australian sport.  I could discuss these differences with out getting irritated.  At the moment?  Not so much.  This particular area of spot is where I have the biggest problems.  Food?  Driving?  Politics? The educational system?  Public transport?  Newspapers? The price of certain things in shops?  The quality of shopping?  I can deal with these and appreciate/live with the differences with out thinking the US is superior. Sport is my problem.

I feel the need to blog about this issue as I need to vent and better learn about my own problems.

A lot of my issues with Australian professional sport revolve around sport related clothing.  Part of the fabric of my life in the Chicago area was going to the mall, going to the shops, going to the park, going to the city and seeing people wearing jumpers and t-shirts and jackets and hats for local sport teams.  Yes, there might be clothing worn for non-local teams but the local teams always dominated.  About 2/3rds of the time, the clothing related to the sport in season.   In Canberra (and to a degree Melbourne and Cairns though I haven’t spent as much time there), I don’t see sport related clothing as much.  When I do see it, I often don’t see it for local teams like the Raiders, the WNBL teams, the local footy (all codes) teams.  I sometimes see AFL shirts and NRL shirts.  I most often see Brumbies shirts.  (The Brumbies aren’t playing right now.)  When I do see baseball hats, they often are for American MLB teams. The AFL grand final is coming up soon and I’m not seeing any increase in club support on the streets of Canberra.  I like that sort of support and passion.  I like to know that people feel proud of their teams enough to wear a shirt.  I love to know that people support Chicago teams, even if they suck.  It just reaffirms our local identity.  I like wearing a Cubs shirt on the train coming back from a Cubs game and having the conductor ask me about a game.

Related to clothing, not since the World Cup have I seen any shops encouraging their staff to wear gear to support their club.  In Chicago, during the NFL season when the Bears play the Packers, a lot of stores (well, my local grocery stores) encourage their staff to wear a jersey for their team.  As a customer, I love this as I can take the piss out of a Packers fan and have fun conversations.  This also feels like it reaffirms our identity as midwesterners and specifically, as Chicagoans (in the greater regional sense).  It makes the area feel less like generic suburbia.

Even more with clothing, I like the fact that in the Chicago area, I can go into a grocery store and buy a Cubs shirt or a Bears shirt or a Blackhawks shirt or a Bulls shirt.  (It all depends on the season and performance.)  I like the fact that there are different pricing tiers so that as some one with no money can buy merchandise.  I like knowing I have the option to spend more.  I like the fact that I can think of ten different places with in two miles of my house at home where I can buy some professional sport related merchandise.  (All important when you’re going to a Sox game and you need a Sox shirt because you’re a Cubs fan.)  I like the fact that in Chicago, almost any tourist related shop you go into will have something related to some professional sport club from the area.  In Gunghalin in the ACT?  I can think of one shop where I can buy NRL  gear (for one team).  There don’t appear to be any places to get AFL gear, ANZ Championship kits, A-League merchandise, NBL or WNBL stuff.  I can think of one place where I can get maybe NBL stuff, and ANZ Championship stuff. I can think of about ten places total in Belconnen and Civic and buy the airport where I can get NRL, AFL and A-League gear.  I can think of maybe one place in those areas where I could get NBL gear.  (I can think of more places to get NBA kits.)  I can’t think of anywhere I can find kits for the Wallaroos or Hockeyroos. Finding merchandise is hard.  When I do find it, it is often at a price point I can’t afford.

Beyond this, when I do wear my limited Australian professional sport clothing out, I often feel like a giant advertisement.  The Raiders are playing this weekend in playoffs for the championship of the NRL.  I don’t see many Raiders shirts.  (Or other NRL team shirts.) (I see more Brumbies gear and that’s still at a low level.)  So when I’m in my loud green Raiders shirt, I feel like I’m sticking out in a way that I just wouldn’t stick out in the US.

I have some issues with that aren’t related to clothing.  One of them is what feels like love of the club you barrack for.  I went to Melbourne this winter.  I got off my train and was trying to catch a tram to St. Kilda right after the game had let out.  If this had been the Chicago area, I would have known immediately who had won.  In Melbourne?  Based on fan reaction of getting out of the stadium?  I had no clue.  Zip.  Zilch.  Nada.  None.  For me, half the fun of going to the game is the utter dejection or utter elation of how your team did.  Random strangers who see you kitted up will ask you how your team did.  You’ll hear people talking about the club and their performance all over.  Making it better, I can be riding a train or sitting in a fast food restaurant and people generally don’t have a problem with me butting in on their sort conversation to give my own two sense.  I love it as it adds to a sense of community identity.  It makes me feel like I’m part of something greater.  It is a way of meeting new people.  I love following sport and feeling that way.

Australia’s major sport seasons appear to have ended for the summer.  I know of a few Australians who have teams who are out of it for the AFL or NRL.  They’ve now moved on to barracking for American sport teams in the NFL.  I just… the loyalties there boggle my mind and the lack of ways of expressing that loyalty in Australia also annoy.  I want to ask at times: Don’t you have Australian sport you (and I) can support?  Don’t you care about the finals of those Aussie leagues to wait until the finals are over before moving on?  And all of that feels really rude and wrong to think… but when my team in Chicago sucks and the time is to move on, I move on to another local team.

I’ve a few more issues with sport culture in Australia that are making me cranky.  I just can’t articulate them all.  A lot of my issues boil down to public passion, community support and willingness to express identity as a sport fan.  I’ve read a bit on dealing with culture shock but I’m not entirely certain how to do it here and be a casual sport nut in Australia like I am in the USA.

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Post #ausvotes analysis and commentary

Posted by Laura on Sunday, 22 August, 2010

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:

#tcot 5
#84 4
#Adelaide 4
#alp 4
#ausvote 4
#conroy 4
#hunglikeaparliament 4
#ope 4
#politics 4
#Quote 4
#ReTweetThisIf 4
#sausagesizzle 4
#votelabor 4
#VoteLiberal 4
#win 4
#wisdom 4
#zing 4
#60 3
#aflhawksfreo 3
#australia 3
#Australian 3
#australianlabor 3
#ausvoted 3
#climate 3
#disability 3
#dontfriskabbott 3
#dontriskRabbit 3
#electiondrinkinggames 3
#electionWire 3
#F1 3
#grayndler 3
#greensaresocialists 3
#hungover 3
#music 3
#nocleanfeed 3
#nowbuggeroff 3
#topend 3
#unintentionallyironic 3
#6RAR 2
#Abbottalypse 2
#abc1 2
#Amnesty 2
#antonygreenfacts 2
#auseats 2
#auselectoralfraud 2
#ausvotes 2
#awunion 2
#Breaking 2
#ChangeTheGovernment 2
#dawson 2
#dontbothertrying 2
#electionnightcocktail 2
#fraser 2
#hescute 2
#ifabbotwins 2
#juliagillard 2
#keysinthefishbowl 2
#LA 2
#labour 2
#lol 2
#losers 2
#masterchef 2
#Melbourne 2
#NBNfail 2
#Note2Females 2
#ozelection 2
#ozlog 2
#p2 2
#Perth 2
#qanda 2
#Qld 2
#rejectedpartynames 2
#retweet 2
#rippedoff 2
#sadbuttrue 2
#SausageSizzles 2
#sbspoll 2
#senate 2
#tallyroom 2
#TeamAmerica 2
#Tehran 2
#travel 2
#truly 2
#TT 2
#twexitpoll 2
#uc 2
#UN 2
#votingpetpeeves 2
#VXToronto 2
#WAfirst 2
#waystoresolvehungparliament 2
#wellhung 2
#wellhungparliament 2
#westwing 2
#wewannaknow 2
#whitsundays 2
#whoneedsdrugs 2
#winwithgodwin 2
#workforitabbott 2
#WorldChampsPosse 2
#WTF 2
#Wyatt2035 2
#xxx 2
#youhaveapotatoforachin 2
#YouKnowYoureInAustralia 2
#zow 2

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:

Tweets Count 48 29 26 11 8

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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#ausvotes Tweet totals by electorates

Posted by Laura on Saturday, 21 August, 2010

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.

State Electorate Liberal Labor Green Family First Sex Party
Australian Capital Territory Canberra 1 1 1 0 0
Australian Capital Territory Fraser 85 84 34 0 0
New South Wales Banks 1 1 0 0 0
New South Wales Barton 1 1 0 0 0
New South Wales Bennelong 0 1 0 0 0
New South Wales Bennelong 0 0 0 0 1
New South Wales Berowra 1 1 0 0 0
New South Wales Bradfield 0 1 0 0 0
New South Wales Calare 2 4 1 0 0
New South Wales Chifley 66 121 7 0 0
New South Wales Cowper 1 1 4 0 0
New South Wales Cunningham 6 6 1 0 0
New South Wales Dobell 1 1 0 0 0
New South Wales Eden-Monaro 1 1 0 0 0
New South Wales Gilmore 7 2 0 0 0
New South Wales Grayndler 7 3 1 0 0
New South Wales Greenway 2 0 0 0 0
New South Wales Hume 1 2 2 0 1
New South Wales Hunter 1 0 0 0 0
New South Wales Kingsford Smith 0 1 1 0 1
New South Wales Lindsay 2 1 3 0 0
New South Wales Lyne 6 17 3 0 0
New South Wales Mackellar 27 85 4 0 0
New South Wales Macquarie 2 1 0 0 0
New South Wales Mitchell 1 1 0 0 0
New South Wales New England 5 6 4 0 2
New South Wales Newcastle 60 124 27 1 1
New South Wales North Sydney 13 47 1 0 0
New South Wales Paterson 17 36 2 0 0
New South Wales Reid 3 3 3 0 0
New South Wales Richmond 3 0 2 0 0
New South Wales Riverina 0 0 1 0 0
New South Wales Robertson 3 10 1 0 0
New South Wales Sydney 343 405 75 4 6
New South Wales Throsby 1 2 0 0 0
New South Wales Wannon 3 2 0 0 0
New South Wales Wentworth 2 4 0 0 0
New South Wales Wills 1 1 0 0 0
Northern Territory Lingiari 1 1 1 0 0
Northern Territory Solomon 1 4 0 0 0
Queensland Blair 0 1 0 0 0
Queensland Brisbane 97 115 20 1 5
Queensland Capricornia 1 1 0 0 0
Queensland Dawson 3 9 2 0 0
Queensland Fairfax 1 1 0 0 0
Queensland Flynn 1 1 0 0 0
Queensland Griffith 1 1 0 0 0
Queensland Herbert 1 2 1 0 0
Queensland Hinkler 0 0 1 0 0
Queensland Kennedy 1 3 0 0 0
Queensland Leichhardt 4 4 2 0 0
Queensland Lilley 0 0 1 0 0
Queensland McPherson 0 1 0 0 0
Queensland Moncrieff 74 231 2 0 0
Queensland Moreton 1 1 1 0 0
Queensland Petrie 1 1 0 0 0
Queensland Ryan 1 2 0 0 0
Queensland Wide Bay 4 2 0 0 0
South Australia Adelaide 85 132 8 3 1
South Australia Barker 0 0 1 0 0
South Australia Bass 2 0 0 0 0
South Australia Boothby 0 1 0 0 0
South Australia Denison 13 1 0 0 0
South Australia Mayo 28 169 3 0 0
South Australia Wakefield 0 1 0 0 0
Tasmania Bass 2 1 0 0 0
Tasmania Denison 13 9 8 0 0
Victoria Ballarat 2 2 1 0 0
Victoria Barton 20 27 23 1 2
Victoria Batman 0 1 0 0 0
Victoria Bendigo 0 0 1 0 0
Victoria Calwell 3 5 1 1 0
Victoria Canning 1 1 0 0 0
Victoria Corangamite 2 1 0 0 0
Victoria Corio 4 3 2 0 0
Victoria Dunkley 1 0 0 0 0
Victoria Flinders 4 31 0 0 0
Victoria Forrest 1 0 0 0 0
Victoria Gellibrand 1 2 0 0 0
Victoria Gorton 1 2 0 0 0
Victoria Higgins 2 1 0 0 0
Victoria Holt 4 1 0 0 0
Victoria Indi 1 2 1 0 0
Victoria Isaacs 2 7 0 0 0
Victoria Jagajaga 0 1 0 0 0
Victoria Kooyong 0 0 1 0 0
Victoria La Trobe 2 2 0 0 0
Victoria Lalor 2 1 1 0 0
Victoria McEwen 0 1 0 0 0
Victoria McMillan 1 0 0 0 0
Victoria Melbourne 244 302 65 8 13
Victoria Melbourne Ports 2 4 1 0 0
Victoria Menzies 3 4 1 0 0
Victoria Murray 0 1 0 0 0
Victoria Scullin 278 367 0 0 0
Western Australia Curtin 88 393 22 3 6
Western Australia Forrest 8 12 1 0 0
Western Australia Fremantle 2 5 2 0 0
Western Australia Hasluck 2 1 0 0 0
Western Australia Moore 1 0 0 0 0
Western Australia Perth 2 1 0 0 0
Western Australia Swan 0 1 0 0 0

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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One last #ausvotes Twitter election related map : Labor vs. Liberal

Posted by Laura on Friday, 20 August, 2010

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:

City States Country Labor Count Liberal Count Green Count
Adelaide South Australia Australia 56 66 6
Alice Springs Northern Territory Australia 1 1 1
Ballarat Victoria Australia 2 1 1
Bathurst New South Wales Australia 1
Bayside Victoria Australia 4 2
Beechworth Victoria Australia 1 1 1
Bellingen New South Wales Australia 1
Blacktown New South Wales Australia 47 54 6
Blue Mountains New South Wales Australia 3
Boroondara Victoria Australia 1
Bowen Hills Queensland Australia 1
Brisbane Queensland Australia 71 69 16
Brunswick West New South Wales Australia 1 1
Bunbury Western Australia Australia 11 8 1
Bundaberg Queensland Australia 1
Burwood New South Wales Australia 3
Cairns Queensland Australia 2 3
Canberra Australian Capital Territory Australia 62 63 33
Carlton Victoria Australia 21 11 21
Chittaway Bay New South Wales Australia 1
Clay Wells South Australia Australia 1
Clovelly New South Wales Australia 1 1
Coffs Harbour New South Wales Australia 1 1
Cremorne New South Wales Australia 1
Darwin Northern Territory Australia 2 1
Enmore New South Wales Australia 2 4
Footscray Victoria Australia 1 1
Fremantle Western Australia Australia 2 2
Geelong Victoria Australia 1 2 2
Gold Coast Queensland Australia 107 37 2
Gordon New South Wales Australia 1
Gosford New South Wales Australia 7 1
Goulburn New South Wales Australia 1 1
Hastings Victoria Australia 7 1
Hobart Tasmania Australia 8 10 7
Illawarra New South Wales Australia 1 1
Indooroopilly Queensland Australia 1
Ipswich Queensland Australia 1
Joondalup Western Australia Australia 1
Katoomba New South Wales Australia 1
Kellyville Ridge New South Wales Australia 2
Kuranda Queensland Australia 1 1 1
Launceston Tasmania Australia 1 2
Lithgow New South Wales Australia 1
Mackay Queensland Australia 5 3 2
Macleod Victoria Australia 1
Mallala South Australia Australia 1
Matraville New South Wales Australia 1
Melbourne Victoria Australia 195 188 63
Mitt New South Wales Australia 1 1
Moe Victoria Australia 1
Mount Druitt New South Wales Australia 3 3
Mt. Isa Queensland Australia 1
Newcastle New South Wales Australia 33 20 9
Newtown New South Wales Australia 1 1 1
North Melbourne Victoria Australia 2 3 1
North Sydney New South Wales Australia 10 4 1
Northcote Victoria Australia 1
Orange New South Wales Australia 2
Parkville Victoria Australia 1 1
Perth Western Australia Australia 157 70 12
Point Cook Victoria Australia 1
Port Macquarie New South Wales Australia 13 5 3
Prahran Victoria Australia 1
Quakers Hill New South Wales Australia 1
Randwick New South Wales Australia 1
Redcliffe Queensland Australia 1 1
Ringwood Victoria Australia 2 1
Robina Queensland Australia 1
Rockhampton Queensland Australia 2 2
Sassafras Victoria Australia 1
Shellharbour New South Wales Australia 1
Shoalhaven New South Wales Australia 3
South Morang Victoria Australia 1
Spence Australian Capital Territory Australia 1
St Leonards Victoria Australia 1
St. Kilda Victoria Australia 1
Strathfield New South Wales Australia 1 3
Sunshine Coast Queensland Australia 4
Surry Hills New South Wales Australia 1
Sydney New South Wales Australia 336 298 87
Torquay Victoria Australia 1
Townsville Queensland Australia 1 1 1
Vermont Victoria Australia 1 1
Wagga Wagga New South Wales Australia 1
Windsor Queensland Australia 1
Wolli Creek New South Wales Australia 1 1
Wollongong New South Wales Australia 4 3 1
Woodside South Australia Australia 3

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Twitter #hashtags and the Australian election

Posted by Laura on Thursday, 19 August, 2010

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:

Tweet Count
#tcot 5157
#ausvotes 2449
#tlot 1816
#p2 1761
#teaparty 1704
#GOP 1172
#ocra 950
#Libertarian 920
#News 867
#ucot 705
#politics 687
#Israel 536
#sgp 536
#iamthemob 367
#jcot 308
#roft 304
#qanda 303
#cdnpoli 293
#Twisters 261
#USA 209
#Obama 194
#MyLiberal 187
#debate 183
#flotilla 171
#Gaza 155
#masterchef 154
#energy 149
#hhrs 147
#green 134
#rpn 121
#912 111
#aus2010 111
#NPC 108
#rootyq 101
#AUSlabor 95
#oilspill 94
#topprog 94
#nocleanfeed 92
#fb 88
#rightriot 80
#laborfail 79
#spill 76
#US 76
#jews 75
#ALP 74
#terrorism 74
#Greens 72
#cspj 70
#tpp 70
#BP 69
#antisemitism 68
#Gillard 67
#p21 67
#openinternet 66
#FF 65
#FollowFriday 65
#oil 65
#Muslim 64
#ireland 63
#Abbott 62
#UK 61
#Australia 58
#Iran 58
#Palin 57
#Blog 55
#Europe 54
#quote 54
#dnc 52
#fail 51
#iranelection 51
#jlot 51
#hcr 47
#MentalHealth 47
#justsayin 46
#rs 46
#eco 44
#boatphone 43
#glennbeck 43
#islam 42
#zionism 42
#NBN 41
#palestine 41
#jobs 40
#environment 39
#Autism 36
#Hamas 36
#Health 36
#military 36
#tiot 36
#dem 35
#AZ 34
#Lebanon 34
#vote2010 34
#dems 33
#ausdebate 32
#bonjovi 32
#climate 32
#judaism 32
#lateline 32
#MoFo 32
#videos 32
#foxnews 31
#theview 31
#730report 30
#hasbara 30
#nz 30
#travel 30
#tweetcongress 30
#YWC 30
#conservative 29
#Gulf 29
#patriottweets 29
#ampat 28
#dublin 28
#property 28
#beck 27
#Free 27
#IDF 27
#ronpaul 27
#acon 26
#jewish 26
#twibbon 26
#bds 25
#CNN 25
#Obamacare 25
#rush 25

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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Australian elections: The geography of Twitter

Posted by Laura on Monday, 19 July, 2010

I wanted to create a map of where people who were following Australian political candidates and/or tweeting about the Australian elections were living. I’ve been getting similar data for Australian sport teams and this feels like a natural extension. I’m not doing an analysis of this data and these maps because while I feel I know enough about Australian sport to draw conclusions, I don’t feel comfortable with Australian politics. If some one else wants to do that or use my raw data, please feel free.

Just as a caveat, this is a limited data set that was collected on the morning of July 19, 2010. I used a tool to mine this and the amount of data included was based on what that tool gave me.

Data Collection Methodology

  1. Identify the major candidates in the Australian elections on Twitter (Tony Abbot, Bob Brown, Julia Gillard) and some of the more popular search phrases and hash tags used by Twitter users (#alp vote, #aus2010, #ausvotes, #laborfail, #myliberal, #nocleanfeed , #qanda, Abbott-Gillard, asylum boat, Gillard, Greens Canberra, Krudd Labor, Labor Liberals, Rudd Liberals, Tony Abbott, Truss LNP, Warren Truss, WorkChoices)
  2. Go to , run a search for those phrases and accounts. Export results to an Excel spread sheet. (Total tweets picked up: 27,506)
  3. Create a new sheet. Copy and paste only those Tweets by people that list a location. (Total tweets included: 26,198)
  4. Using location information from profiles, try to determine the city, state, country that the person lives in. Example: Melbourne = Melbourne, Victoria, Australia. ChiTown = Chicago, Illinois, United States. Brizzy = Brisbane, Queensland, Australia. 32.38504,-83.647406 = Perry, Georgia, United States. Oz = Australia.

View full map

Methodology: Gillard, Abbot, Brown, Truss

  1. On Excel, turn AutoFilter on.
  2. Filter the Tweet column to contain one of the following terms: Gillard, Abbot, Brown, Truss
  3. Copy and paste the City, State, Country columns to a new worksheet. Select those columns on the new worksheet.
  4. Sort data by city. Remove all locations that do not include City information.
  5. Go to Advance Filter. Copy the Unique Records to Another Location.
  6. Using a COUNTIF formula, count the number of tweets that mentioned the term by city.
  7. Go to and copy paste the city, state, column, count in the box for step 1. Click on the Map Now button.
  8. When Geocoder is finished running, select the Show Geocode button, and copy and paste that data to an Excel worksheet. Save each politician’s tweet references on its own CSV file.
  9. Go to and import each csv file. Finish processing each file using latitude and longitude coordinates.

Go to and create one map for all four politicians. Use visual theme, sizes.

Please note this map is based on total tweets by location. It is not total individuals who tweeted with the selected keyword.

View full map

Methodology: Labor, Liberals, Greens, National Party
This methodology is a repeat of the previous except the names of the politicians are replaced with the names of the parties.

View full map

Methodology: Education, Work Choices, Asylum
This methodology is a repeat of the previous except the keywords are: Education OR Schools, Work Choices OR WorkChoices, Immigrant OR Asylum, NTB or Broadband, Environment OR Global Warming, Health OR Doctor, Afghanistan OR Diggers.

Other: Follows
I’m planning on adding two maps to this post tomorrow. One shows the location of followers for Tony Abbott, Julia Gillard and Bob Brown. The other map will include location of tweets relating to certain issues.

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