The political blogosphere and the 2004 U.S. election: divided they blog
Proceedings of the 3rd international workshop on Link discovery
Characterizing user behavior in online social networks
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
An unobtrusive behavioral model of "gross national happiness"
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Characterizing debate performance via aggregated twitter sentiment
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Predicting the Future with Social Media
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Computing political preference among twitter followers
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Democrats, republicans and starbucks afficionados: user classification in twitter
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
The personality of popular facebook users
Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work
Tracking "gross community happiness" from tweets
Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work
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In modern politics, parties and individual candidates must have an online presence and usually have dedicated social media coordinators. In this context, we study the usefulness of analysing Twitter messages to identify both the characteristics of political parties and the political leaning of users. As a case study, we collected the main stream of Twitter related to the 2010 UK General Election during the associated period--gathering around 1,150,000 messages from about 220,000 users. We examined the characteristics of the three main parties in the election and highlighted the main differences between parties. First, Lab our members were the most active and influential during the election while Conservative members were the most organized to promote their activities. Second, the websites and blogs that each political party's members supported are clearly different from those that all the other political parties' members supported. From these observations, we develop a simple and practical classification method which uses the number of Twitter messages referring to a particular political party. The experimental results showed that the proposed classification method achieved about 86% classification accuracy and outperforms other classification methods that require expensive costs for tuning classifier parameters and/or knowledge about network topology.