Liked and shared tweets during the pandemic: the relationship between intrinsic message features and (mis)information engagement

Jiyoung Lee, Youllee Kim and Xun Zhu

Behaviour and Information Technology, 2024, vol. 43, issue 8, 1596-1613

Abstract: This study examines how intrinsic message characteristics (i.e. call to action, negative emotions, and informality) drive online information engagement (i.e. the numbers of likes and retweets). We also explore how the veracity of information (i.e. misinformation vs. accurate information) is intertwined with negative emotions in promoting online information engagement. Analyses of 472 tweets demonstrate that tweets with higher anger-laden words were more likely to be retweeted. By contrast, those with higher anxiety-laden words and more informal language were more likely to receive likes. Regarding the interaction between distinct emotions and the veracity of information, the results showed that anger-laden misinformation tended to be retweeted more and received more likes from users whereas anger-laden accurate information received fewer likes. Further, anxiety-laden accurate information received more likes. This study contributes to existing scholarship surrounding online information engagement by illustrating how intrinsic message features are differentially associated with liking and retweeting behaviours. Our findings also provide practical implications on how messages should be tailored to encourage engagement on social media and devise effective countermeasures to curb the potentially detrimental effects of anger when used for the purposes of misinformation.

Date: 2024
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DOI: 10.1080/0144929X.2023.2222192

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