In this work, we study the use of Twitter by House, Senate and gubernatorial candidates during the midterm (2010) elections in the U.S. Our data includes almost 700 candidates and over 690k documents that they produced and cited in the 3.5 years leading to the elections. We utilize graph and text mining techniques to analyze differences between Democrats, Republicans and Tea Party candidates, and suggest a novel use of language modeling for estimating content cohesiveness. Our findings show significant differences in the usage patterns of social media, and suggest conservative candidates used this medium more effectively, conveying a coherent message and maintaining a dense graph of connections. Despite the lack of party leadership, we find Tea Party members display both structural and language-based cohesiveness. Finally, we investigate the relation between network structure, content and election results by creating a proof-of-concept model that predicts candidate victory with an accuracy of 88.0%.

1. The Party is Over Here: Structure and Content in the 2010 Election Avishay Livne, Matt Simmons, Eytan Adar, Lada Adamic University of Michigan ICWSM 2011 1

2. Twitter and Politics 14% of Internet users engaged in political activity via social media in 2008 => 22% in 2010 [Smith 2008, 2010]. Political Polarization on Twitter – [Conover et al. 2011] Characterizing Debate Performance via Aggregated Twitter Sentiment – [Diakopoulos& Shamma2010] 2 Seal of Approval

3. Twitter in Politics Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment – [Tumasjan et al. 2010] Hypothesis: Portion of tweets mentioning a party => portion of votes this party will get. Achieved MAE of 1.65%, traditional polls achieved 0.8%-1.48%. German federal elections 2009. In the same time a German octopus was used to predict the World Cup ‘10 results... … correctly predicting 8/8 matches Paul the Octopus 3 Our goal: Can we extract more information from Twitter?

4. Agenda Background Data Network analysis Content analysis Predicting election results 4

5. Getting The Data 5 Unlike other studies we looked at candidates’ accounts. Search: “Harry Reid site:twitter.com”

6. 687 manually filtered users, 50% of the candidates. Data 6 *Tea Party – political movement, supported Republicans. Gained wide attention in 2010.

7. Data 460K tweets over 2 years + 233K URLs. 4429 directed edges: AB == user A follows user B 7 Health Care Reform Bill Passed

8. Usage Analysis 8

9. Agenda Background Data Network analysis Content analysis Predicting election results 9

10. Graph Analysis Edge(A,B) = A follows B Democrats Republicans TeaParty 10

11. Graph Analysis Democratic Republican TeaParty 𝐷𝑒𝑛𝑠𝑖𝑡𝑦=𝐸𝑑𝑔𝑒𝑠𝑃𝑜𝑠𝑠𝑖𝑏𝑙𝑒 𝐸𝑑𝑔𝑒𝑠=𝐸𝑁𝑁−1 Supports previous studies [Adamic & Glance 2005] 11

12. Agenda Background & data Network analysis Content analysis Predicting election results 12

13. Language Modeling How frequent each candidate/party used each term? 13 Extract distinguishing terms with KL-divergence

14. Divergent terms 14

15. Party Cohesiveness How similar are pairs within each party 15 Similar Not similar

16. Language Similarity vs. Graph Distance 16 With retweets Without retweets Not similar Similar The closer two candidates are the more similar their content is. This trend diminishes after 3 steps.

17. Latent Topics 17 Distribution of documents over topics Topics 1 LDA Documents Documents 2 Average over party’s documents Distribution of parties over topics Topics Which topics are more affiliated with each party Compare distributions Parties

18. Topics 18

19. Agenda Background & data Network analysis Content analysis Predicting election results 19

20. Predicting Election Results - Individual Features 20 External Network Usage Content Logistic regression, 10-fold cross validation.

21. Predicting Election Results Models Comparison 21 Next, top model…

22. 22 Predicting Election Results Top model’s coefficients

23. Summary Twitter is prevalent in campaigns Tea party more aggressive and Twitter-savvy Republicans more aligned (network) Democrats less cohesive (content) Content similarity vs. network distance Network and language improve prediction 23

24. Future Work More extensive topic & sentiment analysis Temporal analysis Integrating funding Integrating constituents (the crowd) 24

25. Thanks Thank you! Thanks to Abe Gong for helpful insights. More information in the paper. Contact info: avishay@umich.edu 25