Data-Mining Twitter for Political Science -Hickman, Alfredo - Honors Thesis
Political Twitter MPSA Slides
Transcript of Political Twitter MPSA Slides
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Birds of a Feather Tweet Together
Partisan Structure in
Online Social Networks
David B. Sparks
Duke University
images from: twitter.com
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Congressional Twitterers
Congressional Research Service (2009)
Republicans : Democrats :: 2 : 1
85 Tweets/day collectively Most status updates offer links to press releases,
etc.
Only 1.4% of Congressional Tweets are replies
Members refer more frequently to their districts whenon recess.
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Social Network Analyses of Congress
Co-sponsorship Fowler 2005, 2006
Community structure and modularity Zhang, et al. 2008, Waugh et al. 2009
Committee and caucus membership
Porter et al. 2005, Victor and Ringe 2009 Interest groups, lobbyist contributions, andmailing lists Grossmann and Dominguez 2009, Koger and Victor
2009, Koger, Masket, and Noel 2009 Ideological scaling
Poole and Rosenthal, Bafumi and Herron 2007, etc.
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Theory/Motivation
Time and attention are limited, so spending theseis costly.
Thus, Following an entity on Twitter is a signalthat the entity is something on which it is worthspending resources.
Thus, there is information about preferencesencoded in Following behavior.
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Hypotheses
Hypothesis 1: The online network of politicalelites is distinctly partisan.
Hypothesis 2: The allocation of attention by non-elites reflects a partisan dimension.
Hypothesis 3: Attention allocation preferencesreflect political ideology.
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H1: Partisan Network
Following network among 133 Senators andRepresentatives
Two major partisan clusters In-group and cross-group ties:
Freemans Segregation Index: 0.59 Log odds ratio: 3.14 Significant at p < 0.001
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H1: Partisan Network
Fuzzy clustering (Kaufman and Rosseeuw 1990)on distance matrix from adjacencies
Probabilities of each observation falling into each oftwo clusters
Good predictor of Democratic partisanship, less
so for Republicans Correctly classifies 74.4% of members, a 9%
improvement over guessing the modal party.
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Hypotheses
Hypothesis 1: The online network of politicalelites is distinctly partisan.
Hypothesis 2: The allocation of attention by non-elites reflects a partisan dimension.
Hypothesis 3: Attention allocation preferencesreflect political ideology.
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Hypotheses
Hypothesis 1: The online network of politicalelites is distinctly partisan.
Hypothesis 2: The allocation of attention bynon-elites reflects a partisan dimension.
Hypothesis 3: Attention allocation preferencesreflect political ideology.
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H2: Partisan Followers
Collected IDs of 117,837 users Following anycongressional account
Mean legislators Followed: 2.2 Mean Followers per legislator: 3,308
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H2: Partisan Followers
If Following behavior is a function ofpartisanship, we would expect to see a tendency
to Follow MCs of only one party. The most prolific Followers tend toward the
center
Generally media outlets and other completists Some prolific Followers do concentrate
exclusively on one Party
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H2: Partisan Followers
Two types of Followers, distinguished byFollowing count
Major Followers Follow more than 10 Memberaccounts 90th percentile and above
Predict number of Republicans Followed, as afunction of Democrats Followed and MajorFollower classification
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Hypotheses
Hypothesis 1: The online network of politicalelites is distinctly partisan.
Hypothesis 2: The allocation of attention by non-elites reflects a partisan dimension.
Hypothesis 3: Attention allocation preferencesreflect political ideology.
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Hypotheses
Hypothesis 1: The online network of politicalelites is distinctly partisan.
Hypothesis 2: The allocation of attention bynon-elites reflects a partisan dimension.
Hypothesis 3: Attention allocationpreferences reflect political ideology.
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H3: Ideological Following
Person-to-group network of Followers andlegislative Twitterers
Principal Component Analysis of the adjacencymatrix, to reduce dimensionality
Generates scores for Followers
and loadings for legislators If ideology explains Following behavior, it should
be encoded in the first few components
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H3: Ideological Following
0011000politipage
0000010PJDente
0000011GR8GLFR
0111010ElderJustice
0000000johnny7268
1111100rawl4senate
1001100derjensen26
0011000politikpundit
cbrangeljohnthunerogerwickerJohnKerryJeffFlakeMarkUdallclairemc
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H3: Ideological Following
First and especially second components appear tohave ideological content
Followers with scores at the extremes of the scaleappear to be highly ideological/partisan.
Implying that Followers are well-sorted by the PCA
Loadings for Member accounts have a clearrelationship with other measures ofideology/partisanship.
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H3: Ideological Following
Linear discriminant analysis
Predicting known partisanship of Members withloadings on first two components
Correctly identifies partisanship in 94.9% of cases.
Linear regression analysis
Predicting roll call-based Optimal Classificationscalings (Lewis and Poole 2000) with secondcomponent loading, controlling for knownpartisanship
OC scaling correlates with 2nd Component at 0.762
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H3: Ideological Following
Regression results indicate a relationship betweenFollower behavior and ideology
Republican ideology predicted better than that ofDemocrats.
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Hypotheses
Hypothesis 1: The online network of politicalelites is distinctly partisan.
Hypothesis 2: The allocation of attention by non-elites reflects a partisan dimension.
Hypothesis 3: Attention allocation preferencesreflect political ideology.
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Conclusions
Hypothesis 1: The online network of politicalelites is distinctly partisan.
Hypothesis 2: The allocation of attention by non-elites reflects a partisan dimension.
Major Followers do not evince partisan leanings Completists?
Amongst those Following a relatively small number ofMembers, there is a negative relationship betweenRepublican and Democratic accounts Followed.
Hypothesis 3: Attention allocation preferencesreflect political ideology.
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Conclusions
Hypothesis 1: The online network of politicalelites is distinctly partisan.
Hypothesis 2: The allocation of attention by non-elites reflects a partisan dimension.
Hypothesis 3: Attention allocation preferences
reflect political ideology. Scaling of Followers appears to correctly identify
partisans/ideologues
Scaling of Members Successfully classifies partisanship
Predicts accepted measure of ideology
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Future Directions
Scaling everything
Challengers?
Corporations?
Diffusion of ideas through re-tweets and @
replies
Communication with constituents
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Thanks.