Visual analysis of WWW2010 Conference

The WWW2010 Conference took place on April 26-30.  I couldn’t attend it but I collected  all the tweets with the hashtag #www2010 from March 31 to May 3. As an image is worth more than a thousand words I made a visual analysis of the Conference from this data.

(Click on the image to see it larger)

Who were the most active Twitters?	What were the most used words?
What were the most cited sites?	What were the shorten urls services used?
What were the most retwittered URL’s? Twitter Papers at the WWW 2010 Conference (22) WWW2010 Twitter Roomstreams (19) RTP Meetup (15) Futureweb (schedule) (13) What is Twitter, a social network or a news media? (12) Elon University/Pew Intenet Project (Futureweb) (11) Durham, a Tobacco Town, Turns to Local Food (10) Fresh Direction: A Farm-to-Table Restaurant (10) Can the Jewish Deli Be Reformed?(10) Open Government and the World Wide Web (10)	What were the most clicked URL’s? (1) xkcd (2.870) Facebook’s Eroding Privacy Policy: A Timeline (1696) Truly W3C Community building at WWW2010 (Part 1) (1577) Web 2.0 Suicide machine (1248) Tim Berners-Lee on the next Web (1085) Privacy and Publicity in the Context of Big Data (1011) Why Twitter Is the Future of News (996) What is Twitter, a Social Network or a News Media? (739) Open Graph protocol (359) WWW2010 Twitter Roomstreams(310)
How often did users twitter by day?	How often did users twitter by the hour?

Highlighted data:

• Users: 3,093 tweets were written by 900 different users, with an average of 1.24 tweets / user.  71.35% of tweets were written by 20% of users   (Paretos’s Law). The most actives users were: @futureweb2010 (125), @LaTerribleLiz (119), @BoraZ ( 96), @fabien_gandon ( 88), @smalljones (67),  @tommyh @olgag (57),  @apisanty (47), @karenchurch.

• URLs: 1,112 urls were found, 549 of them were different. There were 845 (76%) shorted URLs. Bit.ly was the most shorten url service used with a share of 65.9%.
• Trends: there is a growing interest in Twitter, it is present in most RTs and clicked URLs. However, it maybe due to people who use twitter being more interested in Twitter.
• RT vs. Clicks: There is not a correlation between RTs and Clicks, as can be seen in the image:

Methology

• Own tools were used to extract data
• The  bit.ly API and the Python-bitly library were used to get the number of clicks for each URL.
• Many eyes web service was used to data visualize

DataSet:

• www2010

(1) This ranking is only for URLs shorted with bit.ly. Others URLs couldn’t  be measured