Politics and Social media:

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

Crystal: Analyzing Predictive Opinions on the Web Swapna Somasundaranswapna@cs.pitt.edu

Politics and Social mediaThe 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

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

Lada A. Adamic, Natalie Glance

Motivation: Social media and Politics2004:Harnessing grass root supportHoward Deans campaignBreaking stories first Anti-Kerry video

2007:

OutlineData collectionAnalysisConclusionsSimilar work

DataWeb log directories_________________________

DataConservative blogsWeb log directories_________________________Liberal blogs

DataConservative blogsWeb log directories_________________________Liberal blogsblog

DataConservative blogsWeb log directories_________________________Liberal blogsblog

DataConservative blogsWeb log directories_________________________Liberal blogsblog1494 Blogs

Citation networkblog

Citation networkblogblogblogblogblog

Analysis: Citation network

Analysis: Citation network91%

Analysis: Citation networkConservative Blogs show a greater tendency to link

Analysis: Citation network84%82%74%67%Conservative Blogs show a greater tendency to link

Analysis: PostsData : Top 20 blogs from each each categoryExtract posts from these for a span of 2.5 months.12470 left leaning, 10414 right leaning posts.

Analysis: Strength of community# of posts in which one blog cited another blogRemove links if fewer than 5 citationsRemove links if fewer than 25 citations

Analysis: Strength of communityRight-leaning blogs have denser structure of strong connections than the left

Analysis: Interaction with mainstream mediaLinks to news articles

Analysis: response to CBS news item

Analysis: Occurrences of names of political figures

Analysis: Occurrences of names of political figuresLeft leaning bloggers spoke more about Republicans and vice versa

People support their positions by criticizing those of the political figures they dislike

ConclusionsClear division of blogosphereLinksTopics and peopleConservative blogs are more likely to link.

Future work/ ExtensionsInclude more blogger types Single/multi author distinctionSpread of topics due to network structure?

Some Similar WorkPolitical 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 linkshttp://www.politicaltrends.info/poltrends/poltrends.phppolitical trend tracker - tracks sentiments in political blogs, and reports daily statistics

Some interesting links:Visualization of the blogosphere during French electionshttp://www.observatoire-presidentielle.fr/?pageid=3http://www.fr2007.com/?page_id=2

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

Crystal: Analyzing Predictive Opinions on the WebSoo-min Kim and Eduard Hovy

OverviewCrystal: Election prediction systemMessages on election prediction websitePredictive opinions Automatically create annotated dataFeature generalization, Ngram featuresSupervised learning

OutlineOpinion typesTask definitionDataResults, Insights

OpinionsJudgment OpinionsI like it/ I dislike itPositive/Negative

Predictive OpinionsIt is likely/ unlikely to happen Belief about the futureLikely/unlikely

OpinionsJudgment Opinions

Sentiment Judgment, Evaluation, Feelings, Emotions This is a good cameraI hate this movie

OpinionsPredictive 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)

TaskPredictive Opinion (Party, valence)

Unit of prediction is message post on the discussion board

Datawww.electionprediction.org

Federal Election - 2004 Calgary-eastEdmonton-Beaumont

DataGold standard: party logo used by author of the postPositive examplesNegative examples?

DataIf you pick a party, all mentions of it => likely to winIf you pick a party, all mentions of other parties => not likely to win

No tagLP=+1Con= -1No tag

Analyzing Prediction: Feature generalizationSimilar to back-off idea

ExperimentsClassify each sentence of the messageRestore party names for PartyParty with maximum valence is the party predicted to win by the message

ResultsBaselines:FRQ: most frequently mentioned party in the messageMJR: most dominant predicted partyINC: current holder of the officeNGR: same as Crystal, only feature generalization step is skippedJDG: same as Crystal, but features are only judgment opinion words

ResultsCrystal is the best performer at both the message and the riding levelEven with reduced features, crystal outperforms JDG system by ~ 4% points

Results: Insights

Results: InsightsMutual Exclusivity Mutual Exclusivity

Results: InsightsSentiment

Results: Insightsdesirability

Results: InsightsModalsModals

Some Similar workPredicting 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.

ConclusionExplored predictive opinionsCreated automatically tagged election dataUsed feature generalization to train classifiers to predict election outcomes

Future work/ExtensionsRelation between judgment opinions and predictive opinionsOther sentiment lexicons?

Thank you!

They explore opinions beyond sentiments their results show, especially over the judgment opinions show that other opinions are importantThe obtained automatically tagged election data