Swapna Somasundaran swapna@cs.pitt

1. Politics and Social media:The Political Blogosphere and the 2004 U.S. election: Divided They BlogCrystal: Analyzing Predictive Opinions on the Web Swapna Somasundaran swapna@cs.pitt.edu

2. The Political Blogosphere and the 2004 U.S. election: Divided They Blog Link based Approach Studies linking patterns between blogs just before the presidential elections Crystal: Analyzing Predictive Opinions on the Web Language based approach Uses Linguistic expression of opinion to predict election results Politics and Social media

3. The Political Blogosphere and the 2004 U.S. election: Divided They Blog Lada A. Adamic, Natalie Glance

4. Motivation: Social media and Politics 2004: • Harnessing grass root support • Howard Dean’s campaign • Breaking stories first • Anti-Kerry video 2007:

7. Outline • Data collection • Analysis • Conclusions • Similar work

8. Data Web log directories _______ _______ ______ _____

9. Data Conservative blogs Web log directories _______ _______ ______ _____ Liberal blogs

10. Data Conservative blogs Web log directories _______ _______ ______ _____ blog Liberal blogs

11. Data Conservative blogs Web log directories _______ _______ ______ _____ blog Liberal blogs

12. Data Conservative blogs 1494 Blogs Web log directories _______ _______ ______ _____ blog Liberal blogs

13. Citation network blog

14. Citation network blog blog blog blog blog

15. Analysis: Citation network

16. Analysis: Citation network 91%

17. Analysis: Citation network Conservative Blogs show a greater tendency to link

18. Analysis: Citation network 84% 82% 74% Conservative Blogs show a greater tendency to link 67%

19. Analysis: Posts Data : • Top 20 blogs from each each category • Extract posts from these for a span of 2.5 months. • 12470 left leaning, 10414 right leaning posts.

20. Analysis: Strength of community # of posts in which one blog cited another blog Remove links if fewer than 5 citations Remove links if fewer than 25 citations

21. Analysis: Strength of community Right-leaning blogs have denser structure of strong connections than the left

22. Analysis: Interaction with mainstream media Links to news articles

23. Analysis: response to CBS news item

24. Analysis: Occurrences of names of political figures

25. Analysis: Occurrences of names of political figures Left leaning bloggers spoke more about Republicans and vice versa People support their positions by criticizing those of the political figures they dislike

26. Conclusions • Clear division of blogosphere • Links • Topics and people • Conservative blogs are more likely to link.

27. Future work/ Extensions • Include more blogger types • Single/multi author distinction • Spread of topics due to network structure • …?

28. Some Similar Work • Political Hyperlinking in South Korea: Technical Indicators of Ideology and Content, Park et al. Sociological Research Online, Volume 10, Issue 3, 2005 • Weblog Campaigning in the German Bundestag Election 2005 , Albrecht et al., ,Social Science Computer Review , Volume 25 ,  Issue 4 ,November 2007 • Friends, foes, and fringe: norms and structure in political discussion networks, Kelly et al., International conference on Digital government research , 2006 • 1000 Little Election Campaigns:Utilization and Acceptance of Weblogs in the Run-up to the German General Election 2005 Roland Abold, ECPR Joint Session., Workshop 9: ‘Competitors to Parties in Electoral Politics, 2006

29. Some interesting links • http://www.politicaltrends.info/poltrends/poltrends.php • political trend tracker - tracks sentiments in political blogs, and reports daily statistics

32. Some interesting links: • Visualization of the blogosphere during French elections • http://www.observatoire-presidentielle.fr/?pageid=3 • http://www.fr2007.com/?page_id=2

33. Some Interesting Links: • Political wiki: • http://campaigns.wikia.com/wiki/Mission_Statement

34. Crystal: Analyzing Predictive Opinions on the Web Soo-min Kim and Eduard Hovy

35. Overview • Crystal: Election prediction system • Messages on election prediction website • Predictive opinions • Automatically create annotated data • Feature generalization, Ngram features • Supervised learning

36. Outline • Opinion types • Task definition • Data • Results, Insights

37. Judgment Opinions “I like it/ I dislike it” Positive/Negative Predictive Opinions “It is likely/ unlikely to happen” Belief about the future Likely/unlikely Opinions

38. Opinions • Judgment Opinions Sentiment Judgment, Evaluation, Feelings, Emotions “This is a good camera” “I hate this movie”

39. Opinions • Predictive Opinions Arguing (Wilson et. al, 2005, Somasundaran el al., 2007) • True (“Iran insists its nuclear program is for peaceful purposes”) • will happen (“This will definitely enhance the sales”) • should be done (“The papers have every right to print them and at this point the BBC has an obligation to print them.”) Speculation (Wilson et al, 2005) • Uncertainty about what may/ may not happen (“The president is likely to endorse the bill”)

40. Task • Predictive Opinion • (Party, valence) • Unit of prediction is message post on the discussion board

42. Data • www.electionprediction.org • Federal Election - 2004 • Calgary-east • Edmonton-Beaumont

43. Data • Gold standard: party logo used by author of the post • Positive examples • Negative examples?

44. Data If you pick a party, all mentions of it => “likely to win” If you pick a party, all mentions of other parties => “not likely to win”

46. LP=+1 No tag Con= -1 No tag

47. Analyzing Prediction: Feature generalization Similar to back-off idea

49. Experiments • Classify each sentence of the message • Restore party names for “Party” • Party with maximum valence is the party predicted to win by the message

50. Results Baselines: • FRQ: most frequently mentioned party in the message • MJR: most dominant predicted party • INC: current holder of the office • NGR: same as Crystal, only feature generalization step is skipped • JDG: same as Crystal, but features are only judgment opinion words