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

Libby Hemphill, Annelise Russell, Angela M. Schöpke-Gonzalez

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


Social media platforms like Twitter enable policymakers to communicate their policy preferences directly and provide a bird's-eye view of their diverse policy agendas. In this article, we leverage politicians’ social media data to study political attention using a supervised machine-learning classifier that detects policy areas in individual tweets. We examine how individual diversity and institutional factors affect differential attention to public policy among members of the U.S. Congress. Our novel approach to measuring policy attention builds on work by the Comparative Agendas Project, in order to study members’ political attention in near real-time and to uncover both intragroup and intergroup differences. Using this classifier, we labeled more than one million tweets and found statistically significant differences in both the level and distribution of attention between parties, chambers, and genders. However, these differences were small enough to suggest that other Congressional members’ characteristics are also at play. We explored institutional factors (e.g., committee assignment, caucus), partisan issue preferences (e.g., issue ownership), and the political environment (e.g., partisan issues, confirmations, etc.) that may help explain the patterns of political attention that appear in Congress's tweets.

Original languageEnglish
Pages (from-to)233-256
Number of pages24
JournalPolicy and Internet
Issue number2
StatePublished - Jun 2021

Bibliographical note

Publisher Copyright:
© 2020 Policy Studies Organization


  • Twitter
  • US congress
  • agenda setting
  • machine learning
  • political attention
  • social media
  • text analysis

ASJC Scopus subject areas

  • Health(social science)
  • Public Administration
  • Health Policy
  • Computer Science Applications


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