Running While Female: Using AI to Track how Twitter Commentary Disadvantages Women in the 2020 U.S. Primaries

32 Pages Posted: 17 Sep 2019

See all articles by Sarah Oates

Sarah Oates

Philip Merrill College of Journalism, University of Maryland

Olya Gurevich

Marvelous AI

Christopher Walker

MarvelousAI

Lucina Di Meco

The Wilson Center

Date Written: August 28, 2019

Abstract

While there is conclusive research that female political candidates are treated unfairly by traditional media outlets, the volume and pace of information flow online make it difficult to track the differentiated treatment for female candidates on social media in real time. This paper leverages human coding and natural language processing to cluster tweets into narratives concerned with policy, ideology, character, identity, and electability, focusing on the Democratic candidates in the 2020 U.S. Presidential primary election. We find that female candidates are frequently marginalized and attacked on character and identity issues that are not raised for their male counterparts, echoing the problems found in the traditional media in the framing of female candidates. Our research found a Catch-22 for female candidates, in that they either failed to garner serious attention at all or, if they became a subject of Twitter commentary, were attacked on issues of character and identity that were not raised for their male counterparts. At the same time, women running for president received significantly more negative tweets from right-leaning and non-credible sources than did male candidates. Following the first Democratic debates, the individual differences between male and female candidates became even more pronounced, although at least one female candidate (Elizabeth Warren) seemed to rise above the character attacks by the end of the first debates. We propose that by using artificial intelligence informed by traditional political communication theory, we can much more readily identify and challenge both sexist comments and coverage at scale. We use the concept of narratives by searching for political communication narratives about female candidates that are visible, enduring, resonant, and relevant to particular campaign messages. A real-time measurement system, developed by MarvelousAI, creates a way to allow candidates to identify and push back against sexist framing on social media and take control of their own narratives much more readily.

Keywords: U.S. elections, campaign, female candidates, social media, bias, Twitter, narratives, artificial intelligence

Suggested Citation

Oates, Sarah and Gurevich, Olya and Walker, Christopher and Di Meco, Lucina, Running While Female: Using AI to Track how Twitter Commentary Disadvantages Women in the 2020 U.S. Primaries (August 28, 2019). Available at SSRN: https://ssrn.com/abstract=3444200 or http://dx.doi.org/10.2139/ssrn.3444200

Sarah Oates (Contact Author)

Philip Merrill College of Journalism, University of Maryland ( email )

Knight Hall
College Park, MD 20742
United States
3014054510 (Phone)

HOME PAGE: http://www.media-politics.com

Olya Gurevich

Marvelous AI ( email )

236 West Portal Drive
#860
San Francisco, CA 94127
United States

HOME PAGE: http://https://marvelous.ai/

Christopher Walker

MarvelousAI ( email )

236 West Portal Drive
#860
San Francisco, CA 94127
United States

Lucina Di Meco

The Wilson Center

Washington DC
United States

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