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/
