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Communication is a fundamental step in the process of political representation, and an influential stream of research reveals that male and female politicians talk to their constituents in very different ways. Recent works show that the impact of gender on communication styles extends to social media. Can these differences be explained by variations in the constituencies that male and female politicians represent and by differing career incentives, or are they driven by deeper factors linked to gender roles in politics?
We take advantage of two approaches that can shed new light on how gender affects political communication. First, we leverage the natural experiment created by multimember districts in the American states, which allows us to hold constant a political constituency and observe how politicians of different genders represent it. For the same purposes, we also look at US Senate pairs and at statewide elected officials in states with a plural executive. This research design, which allows us to study politicians at different rungs of the progressive ambition career ladder, also allows us to determine whether gender differences may be driven by varying career incentives. If women and men still communicate differently, even when they are speaking to the same constituents and occupy the same offices, we can be more confident that their communication are driven by factors rooted in gender.
To build the broad dataset necessary to undertake this analysis, we harness the massive trove of communication by American politicians through Twitter. We adopt a supervised learning approach that begins with the hand-coding of over 10,000 tweets, and then use these to train machine learning algorithms to categorize the full corpus of all tweets sent by the legislative, statewide, and US Senate officeholders in our sample. We show that text analysis of tweets can be used to compare communication across many levels of government and shed new light on how gender shapes the way political leaders speak to the public.
Thad Kousser, University of California, San Diego
Daniel M. Butler, University of California, San Diego