Gender and vote revelation strategy in the United States Congress

Cormack, Lindsey

doi:10.1080/09589236.2015.1078228

Cited by 15 publications

(1 citation statement)

References 50 publications

(18 reference statements)

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“…Our results suggest that party, gender, and chamber affect the policy areas that members tweet about. We also compared the diversity of political attention among lawmakers and our findings build and expand on research that suggests Democrats, Representatives, and women are generally more likely to post policy‐related tweets (Cormack, 2016b; Evans, Ovalle, & Green, 2016; Straus, Williams, Shogan, & Glassman, 2014); Democrats and Senators exhibited significantly more diversity than Republicans and Representatives. The specific policies that lawmakers address, and the timing of their attention, likely depend on legislative debates and their committee and caucus memberships.…”

Section: Introductionmentioning

confidence: 67%

What Drives U.S. Congressional Members’ Policy Attention on Twitter?

Hemphill¹,

Russell²,

Schöpke‐Gonzalez³

2020

Policy & Internet

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Understanding how Members of Congress (MCs) distribute their political attention is key to a number of areas of political science research including agenda setting, framing, and issue evolution. Tweets illuminate what lawmakers are paying attention to by aggregating information from newsletters, press releases, and floor debates to provide a birds-eye view of a lawmaker's diverse agenda. In order to leverage this data efficiently, we trained a supervised machine learning classifier to label tweets according to the Comparative Agenda Project's Policy Codebook and used the results to examine the differential attention that policy topics receive from MCs. The classifier achieved an F1 score of 0.79 and a Cohen's kappa with human labelers of 0.78, suggesting good performance. Using this classifier, we labeled 1,485,834 original MC tweets (Retweets were excluded) and conducted a multinomial logistic regression to understand what influenced the policy areas MCs Tweeted about. Our model reveals differences in political attention along party, chamber, and gender lines and their interactions. Our approach allows us to study MCs' political attention in near real-time and to uncover both intra-and inter-group differences.

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Section: Introductionmentioning

confidence: 67%