Understanding the spread ofCOVID‐19 misinformation on social media: The effects of topics and a political leader's nudge

Abstract

The spread of misinformation on social media has become a major societal issue during recent years. In this work, we used the ongoing COVID‐19 pandemic as a case study to systematically investigate factors associated with the spread of multi‐topic misinformation related to one event on social media based on the heuristic‐systematic model. Among factors related to systematic processing of information, we discovered that the topics of a misinformation story matter, with conspiracy theories being the most likely to be retweeted. As for factors related to heuristic processing of information, such as when citizens look up to their leaders during such a crisis, our results demonstrated that behaviors of a political leader, former US President Donald J. Trump, may have nudged people's sharing of COVID‐19 misinformation. Outcomes of this study help social media platform and users better understand and prevent the spread of misinformation on social media.

Understanding How and by Whom COVID-19 Misinformation is Spread on Social Media: Coding and Network Analyses

“…Interestingly, misinformation related to international issues accounted for 2.61% (299/11,450) of all posts but achieved alarmingly higher attention (with an average of 82 forwards, 67 comments, and 713 likes), suggesting that misinformation involving international issues tends to go viral on social media and thus can have serious consequences. This is consistent with empirical findings on Twitter, where COVID-19–related conspiracy misinformation is most likely to spread [ 47 ].…”

“…Unlike rumor-spreading on Twitter, in which the news is usually first posted by a low-impact user and then shared by some popular users [ 26 ], the majority of COVID-19 misinformation on Weibo was represented by the radiation dissemination network, in which the messages were first posted by a prominent user and then directly shared by many general users. In addition, the original user tended to have higher authority (public organizations and news media), suggesting the crucial role of influential users in the spread of COVID-19 misinformation; this is consistent with the results of Wang et al [ 47 ], who found that Donald Trump’s tweets potentially influenced people’s information-sharing behavior.…”

“…In particular, some misinformation about the untested prevention measures and treatment seemed extremely positive and captured the public’s attention. Our results support the idea that misinformation topics should be considered when designing misinformation interventions during the pandemic [ 47 ].…”

Investigations of disaster information representation from a geospatial perspective: Progress, challenges and recommendations

“…In terms of misinformation handling, a set of pandemic misinformation keywords (e.g., do not stay at home, city lockdown) can be developed, and then deep approaches, such as convolutional or recurrent neural network models, can be designed for automatically identifying and filtering misinformation from social media (Ajao, Bhowmik, & Zargari, 2018; Liu & Wu, 2018). Moreover, it is also meaningful to analyze the patterns and factors influencing misinformation dissemination from a geospatial analysis perspective (Forati & Ghose, 2021; Wang, Zhang, Fan, & Zhao, 2021).…”

A Reflection On Disinformation Management for Nurse Leaders

“…Because the stakes are high, the demand for accuracy and clarity of information accelerates in a crisis. 17 The leader must be both aware and available to further learning related to critical issues to both communicate and act in a way that demonstrates an appropriate response (Figure 3).…”