Twitter provides a direct method for political actors to connect with citizens, and for those citizens to organize into online clusters through their use of hashtags (i.e., a word or phrase marked with # to identify an idea or topic and facilitate a search for it). We examine the political alignments and networking of Twitter users, analyzing 9 million tweets produced by more than 23,000 randomly selected followers of candidates for the U.S. House and Senate and governorships in 2010. We find that Twitter users in that election cycle did not align in a simple Right-Left division; rather, five unique clusters emerged within Twitter networks, three of them representing different conservative groupings. Going beyond discourses of fragmentation and polarization, certain clusters engaged in strategic expression such as “retweeting” (i.e., sharing someone else’s tweet with one’s followers) and “hashjacking” (i.e., co-opting the hashtags preferred by political adversaries). We find the Twitter alignments in the political Right were more nuanced than those on the political Left and discuss implications of this behavior in relation to the rise of the Tea Party during the 2010 elections.

With the emergence of the Arab Spring and the Occupy movements, interest in the study of movements that use the Internet and social networking sites has grown exponentially. However, our inability to easily and cheaply analyze the large amount of content these movements produce limits our study of them. This article attempts to address this methodological lacuna by detailing procedures for collecting data from Facebook and presenting a class of computer-aided content analysis methods. I apply one of these methods in the analysis of mobilization patterns of Egypt's April 6 youth movement. I corroborate the method with in-depth interviews from movement participants. I conclude by discussing the difficulties and pitfalls of using this type of data in content analysis and in using automated methods for coding textual data in multiple languages.

With the advent of Twitter and the ability to collect large datasets from this technology, researchers have the opportunity to analyze political participation in cross-national electoral contexts. This paper capitalizes on this capability to examine political polarization and citizen engagement during the US and French presidential campaigns. We use the Twitter Gardenhose collection to filter tweets based on keywords around a 50-day windowrespectively. From these data, we constructed partisan alignments based on hashtag usage and retweet networks. We found evidence of more stark political polarization in the French case, while the US case demonstrated less partisan division. This study elaborates commonalities and contrasts in the use of a major social medium by citizens in contexts that differ in political culture and language but feature similar ideological divides, electoral politics, and campaign contexts. We conclude by discussing the implications of computational social science and "big data" in communications, comparative politics, and political sociology.

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Twitter provides a new and important tool for political actors. In the 2010 midterm elections, the vast majority of candidates for the U.S. House of Representatives and virtually all candidates for U.S. Senate and governorships used Twitter to reach out to potential supporters, direct them to particular pieces of information, request campaign contributions from them, and mobilize their political action. Despite the level of activity, we have little understanding of what the political Twitterverse looks like in terms of communication and discourse. This project seeks to remedy that lack of understanding by mapping candidates and their followers according to their use of hashtags (keywords) and user mentions (direct mentioning of other Twitter users). We have a unique data set constructed from tweets of most of the candidates running for the U.S. House of Representatives in 2010, all the candidates for the Senate and governorships, and a random sample of their followers. From this we utilize multidimensional scaling to construct a visual map based on hashtag and user mention usage. We find that our data have both local and global interpretations that reflect both political leaning and strategies of communication. This study provides insight into innovation in new media usage in political behavior in particular and a bounded topic space in general.