Ozzie Sport

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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While on vacation, a friend of mine recommended I read Want to be Retweeted? Add Hashtags to Your Tweets! The article is rather interesting and looks at the #hashtags that led to the most ReTweets.

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 is a follow up to AFL fan community sentiment: Are fans happy or sad? where I’ve mapped the location of Twitter followers using the data set referenced in the post. The methodology for data collection is outlined there. For tweets that were labeled both happy and sad, they were counted once for each emotion.

View full map

Interesting to note: When viewing the whole of the country, Adelaide SA and Albury VIC are the places where happiness dominates more than other places. When zooming into more, Alberton SA, Dean VIC, Endeavor Hills VIC, Frankston South VIC, Sunshine Coast QLD are the happy places. Given the state of the AFL ladder, that Adelaide has more happy tweets than sad tweets is a bit surprising.

Any other observations? Do people trust sentiment analysis when it comes to sport? Is there a way that this could be refined to make it more trustworthy?

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I haven’t done an update in a month or so… but this is my list of Australian clubs, leagues and athletes on Twitter.  It is sorted by followers.  The list is rather comprehensive but not all comprehensive.  A few accounts have been deleted since the last time I did this.  Those accounts aren’t noted this time around. And unlike the last time around, I haven’t htmled the Twitter accounts.  If you see any people or clubs or fansites that I’m missing, let me know in the comments.

One of the comments included links to several Australian athletes on Twitter that I had missed. Some of these athletes aren’t active in Australian leagues so they don’t necessarily really fit well with most of the people and leagues on the list… but included below for interest. Any more names of people, leagues and clubs I’m missing, let me know.

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I generally don’t believe in sentiment analysis.  I tend to think it is junk because so much of automated sentiment analysis misses contextual clues, relying too heavily on keywords.  Sentiment analysis also tends to give sentiment to bot generated posts and to posts that are neutral.  Despite this, I thought it might be a bit interesting to try to do a sentiment analysis of the AFL fan community on Twitter.  Are they happy or are they sad?  And later, where are the happy fans and where are the sad fans?

The first step in doing that was to collect a whole bunch of AFL related tweets using searchtastic.  I’m currently up around 3,400 tweets. (When/If I do the geolocation version, I’ll provide the raw data set.)  The second step was to develop a list of AFL specific sentiment related words.  In my case, I’m just going with the characteristic of happy and sad to make this easier.  My sentiment keyword list is as follows:

The next step was to give a happy or sad label for tweets that included these terms. The last step was to count up how many Tweets were labeled Happy, Happy / Sad, Sad, No Sentiment.  I generated the following table:

Later, I’m hoping to match this sentiment with the geographic location of the tweets to find out where the pockets of AFL happiness are versus the pockets of AFL sadness are.

Related Posts:

• One week Twitter change for Australia/New Zealand sport related accounts
• Most popular Australian (and New Zealand) sport related Twitter accounts (December 1)
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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 http://www.searchtastic.com/ , 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 http://www.batchgeo.com/ 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 http://finder.geocommons.com/ and import each csv file. Finish processing each file using latitude and longitude coordinates.

Go to http://maker.geocommons.com/ 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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After I did A map of Team GWS on Facebook, which included a comment in the follow up about Twitter location, I wanted to do a similar map for other AFL teams using Twitter.

The problem is I can’t easily get all the follower data on Twitter in a way that is easy for me to work with. Instead, I used a tool called Searchtastic to get profile information about people who mentioned specific keywords in their Tweets. Searchtastic pulls that information from Twitter’s Search API and then outputs in Excel. Each Twitter search I did I related to a team: #gosaints for St. Kilda, Lions AFL for the Brisbane lions. (I tried to do at least three Twitter searches for each team to maximize my data set size.) The raw data used in this post can be found at AFLTweets.xls. After that was done, I went through the location field and determined the city, state, country for everyone who had listed a location. I then chose a few teams, added up how many references by city and created the map below. With the particular tool I’m using, I can only add about 7 or 8 teams before I can’t add more. The teams in this sample include: Adelaide Crows, Brisbane Lions, Carlton Blues, Collingwood Magpies, Geelong Cats, and St. Kilda Saints.

View full map

Things that strike me as interesting: No one from the Northern Territories and only one person from Tasmania and the ACT tweeted about the AFL. Is this because they have less internet access, fewer people, use social networks other than Twitter, or aren’t interested in AFL and these particular clubs? Western Australia also had tiny representation here with the max people per team from the city being one. The state has two teams but fans of the league in the state weren’t mentioning any of the competition. What also strikes me as interesting is that interest in the AFL in Queensland only goes as far north as Maroochydore. Beyond that, nothing. Brisbane suburbs are also largely absent. I should finish the rest of the teams and see if I can’t get a bigger data set to see if that effects the results at all.

Related Posts:

• One week Twitter change for Australia/New Zealand sport related accounts
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This is the third post in a series of creative research data dumps that relate to the Socceroos.

LinkedIn

LiveJournal and clones

This table looks at activity patterns for LJ comms…

MySpace

Age break down of some accounts’s friends…

Orkut

Twitter
Looking for growth information…

YouTube

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