Purpose

The 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/approach

The 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.

Findings

The 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/implications

Only 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/value

The 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 review

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-03-2021-0121/

中文翻译：

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愤怒、悲伤的盛行：在大规模枪击事件中参与错误信息、纠正和情绪化推文

目的

本研究的目的是检查包含不同情绪（即情绪性推文）和不同信息类型（即错误信息、纠正信息等）的推文在大规模枪击事件的初始阶段是如何普遍存在的，以及用户如何参与那些推文。

设计/方法/方法

在埃尔帕索和代顿大规模枪击事件发生后，研究人员手动编码了 2019 年 8 月 3 日至 11 日之间发布的 1,478 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。

发现

作者发现，在 Twitter 上，错误信息在危机期间比纠正更普遍，并且大部分错误信息具有负面情绪（即愤怒、悲伤和焦虑），而纠正则以愤怒为特征。值得注意的是，表达悲伤的推文更有可能被用户转发和喜欢，但包含其他情绪（即愤怒、焦虑和快乐）的推文不太可能被转发和喜欢。

研究限制/影响

只有一部分较大的对话是手动编码的。然而，目前的研究提供了关于在错误信息和纠正方面如何在危机期间传播推文的总体情况，此外，情绪和信息类型如何影响参与行为。

原创性/价值

关于大规模枪击事件的普遍充满愤怒的推文可能会助长敌对叙事，并最终重新引发政治两极分化。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，悲伤的表现可能会导致个人依赖虚假陈述作为应对不确定性的应对策略。

同行评审

本文的同行评审历史可在以下网址获得：https://publons.com/publon/10.1108/OIR-03-2021-0121/