Politics and Social media: The Political Blogosphere and the 2004 U.S. election: Divided They Blog...

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

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

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:

5

6

7

Outline

• Data collection

• Analysis

• Conclusions

• Similar work

8

Data

Web log directories_________________________

Web log directories_________________________

9

DataConservative blogs

Conservative blogs

Web log directories_________________________

Web log directories_________________________ Liberal blogsLiberal blogs

10

DataConservative blogs

Conservative blogs

Web log directories_________________________

Web log directories_________________________ Liberal blogsLiberal blogs

blogblog

11

DataConservative blogs

Conservative blogs

Web log directories_________________________

Web log directories_________________________ Liberal blogsLiberal blogs

blogblog

12

DataConservative blogs

Conservative blogs

Web log directories_________________________

Web log directories_________________________ Liberal blogsLiberal blogs

blogblog

1494 Blogs

13

Citation network

blogblog

14

Citation network

blogblogblogblog

blogblog

blogblogblogblog

15

Analysis: Citation network

16

Analysis: Citation network

91%

17

Analysis: Citation network

Conservative Blogs show a greater tendency to link

18

Analysis: Citation network84%

82%

74%

67%

Conservative Blogs show a greater tendency to link

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

30

31

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

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

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

41

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”

45

46

No tag LP=+1

Con= -1

No tag

47

Analyzing Prediction: Feature generalization

Similar to back-off

idea

48

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

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

Results: Insights

53

Results: Insights

Mutual Exclusivity

Mutual Exclusivity

54

Results: Insights

Sentiment

55

Results: Insights

desirability

56

Results: Insights

Modals Modals

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

Conclusion

• Explored predictive opinions

• Created automatically tagged election data

• Used feature generalization to train classifiers to predict election outcomes

59

Future work/Extensions

• Relation between judgment opinions and predictive opinions

• Other sentiment lexicons

• …?

60

Thank you!