Assembling the Networks and Audiences of Disinformation: How Successful Russian IRA Twitter Accounts Built Their Followings, 2015–2017

Author:

Zhang Yini1ORCID,Lukito Josephine2ORCID,Su Min-Hsin3,Suk Jiyoun3ORCID,Xia Yiping3,Kim Sang Jung3,Doroshenko Larissa4,Wells Chris5ORCID

Affiliation:

1. Department of Communication, University at Buffalo, Buffalo, NY, USA

2. School of Journalism and Media, University of Texas at Austin, Austin, TX, USA

3. School of Journalism & Mass Communication, University of Wisconsin-Madison, Madison, WI, USA

4. College of Arts, Media and Design, Northeastern University, Boston, MA, USA

5. Division of Emerging Media Studies, Boston University, Boston, MA, USA

Abstract

Abstract This study investigates how successful Russian Internet Research Agency (IRA) Twitter accounts constructed the followings that were central to their disinformation campaigns around the 2016 U.S. presidential election. Treating an account’s social media following as both an ego network and an audience critical for information diffusion and influence accrual, we situate IRA Twitter accounts’ accumulation of followers in the ideologically polarized, attention driven, and asymmetric political communication system. Results show that partisan enclaves on Twitter contributed to IRA accounts’ followings through retweeting; and that mainstream and hyperpartisan media assisted conservative IRA accounts’ following gain by embedding their tweets in news. These results illustrate how network dynamics within social media and news media amplification beyond it together boosted social media followings. Our results also highlight the dynamics fanning the flames of disinformation: partisan polarization, media fragmentation and asymmetry, and an attention economy optimized for engagement rather than accuracy.

Publisher

Oxford University Press (OUP)

Subject

Linguistics and Language,Language and Linguistics,Communication

Reference71 articles.

1. Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks;Aral;Proceedings of the National Academy of Sciences of the United States of America,2009

2. Characterizing the 2016 Russian IRA influence campaign;Badawy;Social Network Analysis and Mining,2019

3. Assessing the Russian Internet Research Agency’s impact on the political attitudes and behaviors of American Twitter users in late 2017;Bail;Proceedings of the National Scademy of Sciences of the United States of America,2020

Cited by 24 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