Gender dynamics on Twitter during the 2020 US Democratic presidential primary

被引:0
|
作者
King, Catherine [1 ]
Carley, Kathleen M. [1 ]
机构
[1] Carnegie Mellon Univ, Software & Soc Syst Dept, 5000 Forbes Ave, Pittsburgh, PA 15213 USA
关键词
Computational social science; Social media analytics; 2020; U; S; election; Abusive language; NEWS;
D O I
10.1007/s13278-023-01045-4
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The 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.
引用
收藏
页数:15
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