Gender dynamics on Twitter during the 2020 U.S. Democratic presidential primary

Author:

King CatherineORCID,Carley Kathleen M.ORCID

Abstract

AbstractThe Twitter social network for each of the top five U.S. Democratic presidential candidates in 2020 was analyzed to determine if there were any differences in the treatment of the candidates. This data set was collected from discussions of the presidential primary between December 2019 through April 2020. It was then separated into five sets,  one for each candidate. We found that the most discussed candidates, President Biden and Senator Sanders, received by far the most engagement from verified users and news agencies even before the Iowa caucuses, which was ultimately won by Mayor Buttigieg. The most popular candidates were also generally targeted more frequently by bots, trolls, and other aggressive users. However, the abusive language targeting the top two female candidates, Senators Warren and Klobuchar, included slightly more gendered and sexist language compared with the other candidates. Additionally, sexist slurs that ordinarily describe women were used more frequently than male slurs in all candidate data sets. Our results indicate that there may still be an undercurrent of sexist stereotypes permeating the social media conversation surrounding female U.S. presidential candidates.

Funder

Office of Naval Research

John S. and James L. Knight Foundation

Center for Computational Analysis of Social and Organizational Systems

Center for Informed Democracy and Social-cybersecurity

Carnegie Mellon University

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Human-Computer Interaction,Media Technology,Communication,Information Systems

Reference44 articles.

1. A Decadal Survey of the Social and Behavioral Sciences (2019) A research agenda for advancing intelligence analysis. National Academies Press. https://doi.org/10.17226/25335

2. Ablett R (2018) ‘Doris, You Bitch’: the sexist and gendered ageist discourses of Twitter users concerning a female-named UK storm. Trent Notes Linguist 1:75–88

3. Allcott H, Gentzkow M (2017) Social media and fake news in the 2016 election. J Econ Perspect 31(2):211–236. https://doi.org/10.1257/jep.31.2.211

4. Altman N, Carley KM, Reminga J (2020) ORA User’s Guide 2020. Tech. Rep. No. CMU-ISR-20-110. Carnegie Mellon University, School of Computer Science, Institute for Software Research, Pittsburgh

5. Ballotpedia (2020) Democratic presidential nomination. Ballotpedia. https://ballotpedia.org/Democratic presidential nomination. Accessed 18 Dec 2021

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3