political twitter mpsa slides

8/8/2019 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





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Hypotheses


Hypothesis 2: The allocation of attention bynon-elites reflects a partisan dimension.



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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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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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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





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Hypotheses


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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0011000politipage

0000010PJDente

0000011GR8GLFR

0111010ElderJustice

0000000johnny7268

1111100rawl4senate

1001100derjensen26

0011000politikpundit

cbrangeljohnthunerogerwickerJohnKerryJeffFlakeMarkUdallclairemc


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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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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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Regression results indicate a relationship betweenFollower behavior and ideology

Republican ideology predicted better than that ofDemocrats.


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Hypotheses





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Conclusions



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.



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Conclusions



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.

political twitter mpsa slides

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