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Online social networks are an important setting for understanding the intersection of online communities and offline political processes. This paper analyzes the different ways that people discuss elections on Twitter. Using data from a random sample totaling 113,985 tweets and 30,995 users, we examine the differences between users who employ various strategies to talk about US 2010 Congressional candidates. We show that users who simply include the text of a candidate's name in a message exhibit different behaviors than those who use platform-specific mechanisms. Users who employ free-text tend to have younger accounts, are less likely to be ‘verified’, and generate fewer messages about candidates. Furthermore, candidates’ share of the free-text Twitter public has a larger correlation with their vote tallies than @mentions or hashtags. This research has methodological implications for studying the dynamics between online discourse and offline behavior. Overall, our findings support the view that forms of...

The intensive research activity in analysis of social media and micro-blogging data in recent years suggests the necessity and great potential of platforms that can efficiently store, query, analyze, and visualize social media data. To support these “social media observatories” effectively, a storage platform must satisfy special requirements for loading and storage of multi-terabyte datasets, as well as efficient evaluation of queries involving analysis of the text of millions of social updates. Traditional inverted indexing techniques do not meet such requirements. As a solution, we propose a general indexing framework, IndexedHBase, to build specially customized index structures for facilitating efficient queries on an HBase distributed data storage system. IndexedHBase is used to support a social media observatory that collects and analyzes data obtained through the Twitter streaming API. We develop a parallel query evaluation strategy that can explore the customized index structures efficiently, and test it on a set of typical social media data queries. We evaluate the performance of IndexedHBase on FutureGrid and compare it with Riak, a widely adopted commercial NoSQL database system. The results show that IndexedHBase provides a data loading speed that is six times faster than Riak and is significantly more efficient in evaluating queries involving large result sets.

The broad adoption of online social networking platforms has made it possible to study communication networks at an unprecedented scale. Digital trace data can be compiled into large data sets of online discourse. However, it is a challenge to collect, store, filter, and analyze large amounts of data, even by experts in the computational sciences. Here we describe our recent extensions to Truthy, a system that collects Twitter data to analyze discourse in near real-time. We introduce several interactive visualizations and analytical tools with the goal of enabling citizens, journalists, and researchers to understand and study online social networks at multiple scales., ACM Computer Supported Cooperative Work '13, Demonstration

Is social media a valid indicator of political behavior? There is considerable debate about the validity of data extracted from social media for studying offline behavior. To address this issue, we show that there is a statistically significant association between tweets that mention a candidate for the U.S. House of Representatives and his or her subsequent electoral performance. We demonstrate this result with an analysis of 542,969 tweets mentioning candidates selected from a random sample of 3,570,054,618, as well as Federal Election Commission data from 795 competitive races in the 2010 and 2012 U.S. congressional elections. This finding persists even when controlling for incumbency, district partisanship, media coverage of the race, time, and demographic variables such as the district's racial and gender composition. Our findings show that reliable data about political behavior can be extracted from social media.

The intensive research activities in social data analysis in recent years suggest the necessity and great potential of a public social data observatory. To effectively support a social data observatory, the storage platform must satisfy its special requirements for loading and storage of Terabyte-level datasets, as well as efficient evaluation of queries involving analysis of the texts of millions of social updates. Traditional inverted indexing techniques do not meet such requirements due to their targeted use cases in text retrieval scenarios. To address these problems, we propose a general indexing framework, IndexedHBase, to build specially customized index structures for facilitating efficient queries, and employ the HBase system for distributed data storage. IndexedHBase is used to support the Truthy system that collects and analyzes data obtained through the Twitter streaming API. To handle the special queries in Truthy, we develop a parallel query evaluation strategy that can explore the customized index structures efficiently. We evaluate the performance of IndexedHBase on FutureGrid, and compare it with Riak, a widely adopted commercial NoSQL database system. The results show that IndexedHBase provides a data loading speed that is 6 times faster than Riak, and is significantly more efficient in evaluating queries involving large result sets.