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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 The Political Blogosphere and the 2004 U.S. election: Divided They Blog Link based Approach

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swapna somasundaran swapna@cs pitt edu

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

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

Politics and Social media
the political blogosphere and the 2004 u s election divided they blog
The Political Blogosphere and the 2004 U.S. election: Divided They Blog

Lada A. Adamic, Natalie Glance

motivation social media and politics
Motivation: Social media and Politics

2004:

  • Harnessing grass root support
    • Howard Dean’s campaign
  • Breaking stories first
    • Anti-Kerry video

2007:

outline
Outline
  • Data collection
  • Analysis
  • Conclusions
  • Similar work
slide8
Data

Web log directories

_______

_______

______

_____

slide9
Data

Conservative blogs

Web log directories

_______

_______

______

_____

Liberal blogs

slide10
Data

Conservative blogs

Web log directories

_______

_______

______

_____

blog

Liberal blogs

slide11
Data

Conservative blogs

Web log directories

_______

_______

______

_____

blog

Liberal blogs

slide12
Data

Conservative blogs

1494 Blogs

Web log directories

_______

_______

______

_____

blog

Liberal blogs

citation network14
Citation network

blog

blog

blog

blog

blog

analysis citation network17
Analysis: Citation network

Conservative Blogs show a greater tendency to link

analysis citation network18
Analysis: Citation network

84%

82%

74%

Conservative Blogs show a greater tendency to link

67%

analysis posts
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.
analysis strength of community
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

analysis strength of community21
Analysis: Strength of community

Right-leaning blogs have denser structure of strong connections than the left

analysis occurrences of names of political figures25
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

conclusions
Conclusions
  • Clear division of blogosphere
    • Links
    • Topics and people
  • Conservative blogs are more likely to link.
future work extensions
Future work/ Extensions
  • Include more blogger types
  • Single/multi author distinction
  • Spread of topics due to network structure
  • …?
some similar work
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
some interesting links
Some interesting links
  • http://www.politicaltrends.info/poltrends/poltrends.php
    • political trend tracker - tracks sentiments in political blogs, and reports daily statistics
some interesting links32
Some interesting links:
  • Visualization of the blogosphere during French elections
    • http://www.observatoire-presidentielle.fr/?pageid=3
    • http://www.fr2007.com/?page_id=2
some interesting links33
Some Interesting Links:
  • Political wiki:
    • http://campaigns.wikia.com/wiki/Mission_Statement
overview
Overview
  • Crystal: Election prediction system
    • Messages on election prediction website
    • Predictive opinions
    • Automatically create annotated data
    • Feature generalization, Ngram features
    • Supervised learning
outline36
Outline
  • Opinion types
  • Task definition
  • Data
  • Results, Insights
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

Opinions
opinions38
Opinions
  • Judgment Opinions

Sentiment

Judgment, Evaluation, Feelings, Emotions

“This is a good camera”

“I hate this movie”

opinions39
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”)

slide40
Task
  • Predictive Opinion
    • (Party, valence)
  • Unit of prediction is message post on the discussion board
slide42
Data
  • www.electionprediction.org
  • Federal Election - 2004
  • Calgary-east
  • Edmonton-Beaumont
slide43
Data
  • Gold standard: party logo used by author of the post
    • Positive examples
    • Negative examples?
slide44
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”

slide46

LP=+1

No tag

Con= -1

No tag

experiments
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
results
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
results51
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
results insights53
Results: Insights

Mutual Exclusivity

Mutual Exclusivity

results insights55
Results: Insights

desirability

results insights56
Results: Insights

Modals

Modals

some similar work57
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.
conclusion
Conclusion
  • Explored predictive opinions
  • Created automatically tagged election data
  • Used feature generalization to train classifiers to predict election outcomes
future work extensions59
Future work/Extensions
  • Relation between judgment opinions and predictive opinions
  • Other sentiment lexicons
  • …?