politics and social media: the political blogosphere and the 2004 u.s. election: divided they blog...
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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!
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