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

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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

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The Political Blogosphere and the 2004 U.S. election: Divided They

Blog

Lada A. Adamic, Natalie Glance

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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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Outline

• Data collection

• Analysis

• Conclusions

• Similar work

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Data

Web log directories_________________________

Web log directories_________________________

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DataConservative blogs

Conservative blogs

Web log directories_________________________

Web log directories_________________________ Liberal blogsLiberal blogs

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DataConservative blogs

Conservative blogs

Web log directories_________________________

Web log directories_________________________ Liberal blogsLiberal blogs

blogblog

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DataConservative blogs

Conservative blogs

Web log directories_________________________

Web log directories_________________________ Liberal blogsLiberal blogs

blogblog

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DataConservative blogs

Conservative blogs

Web log directories_________________________

Web log directories_________________________ Liberal blogsLiberal blogs

blogblog

1494 Blogs

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Citation network

blogblog

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Citation network

blogblogblogblog

blogblog

blogblogblogblog

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Analysis: Citation network

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Analysis: Citation network

91%

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Analysis: Citation network

Conservative Blogs show a greater tendency to link

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Analysis: Citation network84%

82%

74%

67%

Conservative Blogs show a greater tendency to link

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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.

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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

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Analysis: Strength of community

Right-leaning blogs have denser structure of strong connections

than the left

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Analysis: Interaction with mainstream media

Links to news articles

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Analysis: response to CBS news item

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Analysis: Occurrences of names of political figures

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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

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Conclusions

• Clear division of blogosphere– Links– Topics and people

• Conservative blogs are more likely to link.

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Future work/ Extensions

• Include more blogger types

• Single/multi author distinction

• Spread of topics due to network structure

• …?

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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

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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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Some interesting links:

• Visualization of the blogosphere during French elections– http://www.observatoire-presidentielle.fr/?pageid=3

– http://www.fr2007.com/?page_id=2

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Some Interesting Links:

• Political wiki:– http://campaigns.wikia.com/wiki/Mission_Statement

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Crystal: Analyzing Predictive Opinions on the Web

Soo-min Kim and Eduard Hovy

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Overview

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

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Outline

• Opinion types

• Task definition

• Data

• Results, Insights

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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

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Opinions

• Judgment Opinions

Sentiment Judgment, Evaluation, Feelings, Emotions

“This is a good camera”

“I hate this movie”

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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”)

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Task

• Predictive Opinion – (Party, valence)

• Unit of prediction is message post on the discussion board

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Data

• www.electionprediction.org

• Federal Election - 2004

• Calgary-east

• Edmonton-Beaumont

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Data

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

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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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No tag LP=+1

Con= -1

No tag

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Analyzing Prediction: Feature generalization

Similar to back-off

idea

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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

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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

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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

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Results: Insights

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Results: Insights

Mutual Exclusivity

Mutual Exclusivity

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Results: Insights

Sentiment

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Results: Insights

desirability

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Results: Insights

Modals Modals

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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.

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Conclusion

• Explored predictive opinions

• Created automatically tagged election data

• Used feature generalization to train classifiers to predict election outcomes

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Future work/Extensions

• Relation between judgment opinions and predictive opinions

• Other sentiment lexicons

• …?

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Thank you!