Prevalence of anger, engaged in sadness: engagement in misinformation, correction, and emotional tweets during mass shootings

Jiyoung Lee, Shaheen Kanthawala, Brian C. Britt, Danielle Deavours, Tanya Ott-Fulmore
{"title":"Prevalence of anger, engaged in sadness: engagement in misinformation, correction, and emotional tweets during mass shootings","authors":"Jiyoung Lee, Shaheen Kanthawala, Brian C. Britt, Danielle Deavours, Tanya Ott-Fulmore","doi":"10.1108/oir-03-2021-0121/v1/decision1","DOIUrl":null,"url":null,"abstract":"PurposeThe goal of this study is to examine how tweets containing distinct emotions (i.e., emotional tweets) and different information types (i.e., misinformation, corrective information, and others) are prevalent during the initial phase of mass shootings and furthermore, how users engage in those tweets.Design/methodology/approachThe researchers manually coded 1,478 tweets posted between August 3–11, 2019, in the immediate aftermath of the El Paso and Dayton mass shootings. This manual coding approach systematically examined the distinct emotions and information types of each tweet.FindingsThe authors found that, on Twitter, misinformation was more prevalent than correction during crises and a large portion of misinformation had negative emotions (i.e., anger, sadness, and anxiety), while correction featured anger. Notably, sadness-exhibiting tweets were more likely to be retweeted and liked by users, but tweets containing other emotions (i.e., anger, anxiety, and joy) were less likely to be retweeted and liked.Research limitations/implicationsOnly a portion of the larger conversation was manually coded. However, the current study provides an overall picture of how tweets are circulated during crises in terms of misinformation and correction, and moreover, how emotions and information types alike influence engagement behaviors.Originality/valueThe pervasive anger-laden tweets about mass shooting incidents might contribute to hostile narratives and eventually reignite political polarization. The notable presence of anger in correction tweets further suggests that those who are trying to provide correction to misinformation also rely on emotion. Moreover, our study suggests that displays of sadness could function in a way that leads individuals to rely on false claims as a coping strategy to counteract uncertainty.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-03-2021-0121/","PeriodicalId":143302,"journal":{"name":"Online Inf. Rev.","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Online Inf. Rev.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/oir-03-2021-0121/v1/decision1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9

Abstract

PurposeThe goal of this study is to examine how tweets containing distinct emotions (i.e., emotional tweets) and different information types (i.e., misinformation, corrective information, and others) are prevalent during the initial phase of mass shootings and furthermore, how users engage in those tweets.Design/methodology/approachThe researchers manually coded 1,478 tweets posted between August 3–11, 2019, in the immediate aftermath of the El Paso and Dayton mass shootings. This manual coding approach systematically examined the distinct emotions and information types of each tweet.FindingsThe authors found that, on Twitter, misinformation was more prevalent than correction during crises and a large portion of misinformation had negative emotions (i.e., anger, sadness, and anxiety), while correction featured anger. Notably, sadness-exhibiting tweets were more likely to be retweeted and liked by users, but tweets containing other emotions (i.e., anger, anxiety, and joy) were less likely to be retweeted and liked.Research limitations/implicationsOnly a portion of the larger conversation was manually coded. However, the current study provides an overall picture of how tweets are circulated during crises in terms of misinformation and correction, and moreover, how emotions and information types alike influence engagement behaviors.Originality/valueThe pervasive anger-laden tweets about mass shooting incidents might contribute to hostile narratives and eventually reignite political polarization. The notable presence of anger in correction tweets further suggests that those who are trying to provide correction to misinformation also rely on emotion. Moreover, our study suggests that displays of sadness could function in a way that leads individuals to rely on false claims as a coping strategy to counteract uncertainty.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-03-2021-0121/
愤怒盛行,陷入悲伤:在大规模枪击事件中,参与错误信息、纠正和情绪化的推文
本研究的目的是研究在大规模枪击事件的初始阶段,包含不同情绪(即情绪性推文)和不同信息类型(即错误信息、纠正信息等)的推文是如何流行的,此外,用户是如何参与这些推文的。研究人员手动编码了2019年8月3日至11日在埃尔帕索和代顿大规模枪击事件发生后发布的1478条推文。这种手动编码方法系统地检查了每条推文的不同情绪和信息类型。作者发现,在Twitter上,在危机期间,错误信息比更正信息更普遍,而且很大一部分错误信息带有负面情绪(即愤怒、悲伤和焦虑),而更正信息则带有愤怒。值得注意的是,表现出悲伤的推文更容易被用户转发和点赞,而包含其他情绪(即愤怒、焦虑和快乐)的推文则不太可能被转发和点赞。研究限制/启示:只有一部分较大的对话是手动编码的。然而,目前的研究从错误信息和纠正方面提供了tweet在危机期间如何传播的总体情况,此外,情绪和信息类型如何影响参与行为。关于大规模枪击事件的无处不在的充满愤怒的推文可能会助长敌意的叙述,并最终重新点燃政治两极分化。更正推文中明显存在的愤怒进一步表明,那些试图纠正错误信息的人也依赖于情绪。此外,我们的研究表明,悲伤的表现可能在某种程度上导致个人依赖虚假声明作为应对策略来抵消不确定性。同行评议本文的同行评议历史可在:https://publons.com/publon/10.1108/OIR-03-2021-0121/
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信