ABSTRACT - 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.
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