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

2. 2 Politics and Social media 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

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

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

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7. 7 Outline Data collection Analysis Conclusions Similar work

8. 8 Data Web log directories _______ ______ _____ Web log directories _______ ______ _____

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

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

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

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

13. 13 Citation network blog

14. 14 Citation network blog

15. 15 Analysis: Citation network

16. 16 Analysis: Citation network 91%

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

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

19. 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. 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. 21 Analysis: Strength of community Right-leaning blogs have denser structure of strong connections than the left

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

23. 23 Analysis: response to CBS news item

24. 24 Analysis: Occurrences of names of political figures

25. 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. 26 Conclusions Clear division of blogosphere –Links –Topics and people Conservative blogs are more likely to link.

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

28. 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. 29 Some interesting links http://www.politicaltrends.info/poltrends/poltrends.php –political trend tracker - tracks sentiments in political blogs, and reports daily statistics

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32. 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. 33 Some Interesting Links: Political wiki: –http://campaigns.wikia.com/wiki/Mission_Statement

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

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

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

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

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

39. 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. 40 Task Predictive Opinion –(Party, valence) Unit of prediction is message post on the discussion board

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

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

44. 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”

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

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

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49. 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. 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

51. 51 Results Crystal is the best performer at both the message and the riding level Even with reduced features, crystal outperforms JDG system by ~ 4% points

52. 52 Results: Insights

53. 53 Results: Insights Mutual Exclusivity

54. 54 Results: Insights Sentiment

55. 55 Results: Insights desirability

56. 56 Results: Insights Modals

57. 57 Some Similar work Predicting Movie Sales from Blogger Sentiment, Mishne and Glance, (2006) AAAI-CAAW 2006 Annotating Attributions and Private States, Wilson and Wiebe (2005). ACL Workshop 2005 QA with Attitude: Exploiting Opinion Type Analysis for Improving Question Answering in On-line Discussions and the News, Somasundaran et al. ICWSM 2007.

58. 58 Conclusion Explored predictive opinions Created automatically tagged election data Used feature generalization to train classifiers to predict election outcomes

59. 59 Future work/Extensions Relation between judgment opinions and predictive opinions Other sentiment lexicons …?

60. 60 Thank you!