Patterns of Incivility on U.S. Congress Members' Social Media Accounts: A Comprehensive Analysis of the Influence of Platform, Post, and Person Characteristics

Author:

Unkel Julian,Kümpel Anna Sophie

Abstract

With social media now being ubiquitously used by citizens and political actors, concerns over the incivility of interactions on these platforms have grown. While research has already started to investigate some of the factors that lead users to leave incivil comments on political social media posts, we are lacking a comprehensive understanding of the influence of platform, post, and person characteristics. Using automated text analysis methods on a large body of U.S. Congress Members' social media posts (n = 253,884) and the associated user comments (n = 49,508,863), we investigate how different social media platforms (Facebook, Twitter), characteristics of the original post (e.g., incivility, reach), and personal characteristics of the politicians (e.g., gender, ethnicity) affect the occurrence of incivil user comments. Our results show that ~23% of all comments can be classified as incivil but that there are important temporal and contextual dynamics. Having incivil comments on one's social media page seems more likely on Twitter than on Facebook and more likely when politicians use incivil language themselves, while the influence of personal characteristics is less clear-cut. Our findings add to the literature on political incivility by providing important insights regarding the dynamics of uncivil discourse, thus helping platforms, political actors, and educators to address associated problems.

Publisher

Frontiers Media SA

Subject

Religious studies,Cultural Studies

Reference66 articles.

1. Correcting measurement error in content analysis;Bachl;Commun. Methods Meas.,2017

2. BarbishV. VaughnK. ChikhladzeM. NielsenM. CorleyK. PalaciosJ. Congress, Constituents, and Social Media: Understanding Member Communications in the Age of Instantaneous Communication2019

3. Social media elements, ecologies, and effects;Bayer;Annu. Rev. Psychol.,2020

4. Using machine learning to advance personality assessment and theory;Bleidorn;Pers. Soc. Psychol. Rev.,2019

5. The digital architectures of social media: comparing political campaigning on Facebook, Twitter, Instagram, and Snapchat in the 2016 U.S. election;Bossetta;J. Mass Commun. Q.,2018

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

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