Fighting misinformation on social media: effects of evidence type and presentation mode.

Designing corrective messages to debunk misinformation online is an important practice toward ending the coronavirus disease (COVID-19) pandemic as health-related misinformation has proliferated on social media misguiding disease prevention measures. Despite research on the use of statistical evidence and message modality in persuasion, the effects of evidence type (assertions with versus without statistical evidence) and presentation mode (text-only versus image-only versus text-plus-image) have been understudied. This study examined the impact of evidence type and presentation mode on individuals' responses to corrective messages about COVID-19 on social media. The results showed that the presence of statistical evidence in assertions reduced message elaboration, which in turn reduced the effects of the message in correcting misperceptions, decreased perceived message believability and lowered social media users' intentions to further engage with and disseminate the corrective message. Compared to the text-only modality and the text-plus-image modality, the image-only modality triggered significantly lower levels of message elaboration, which subsequently heightened message believability and increased user engagement intentions. The theoretical and practical implications are discussed.

A Novel Machine Learning Framework for Comparison of Viral COVID-19-Related Sina Weibo and Twitter Posts: Workflow Development and Content Analysis.

BACKGROUND: Social media plays a critical role in health communications, especially during global health emergencies such as the current COVID-19 pandemic. However, there is a lack of a universal analytical framework to extract, quantify, and compare content features in public discourse of emerging health issues on different social media platforms across a broad sociocultural spectrum. OBJECTIVE: We aimed to develop a novel and universal content feature extraction and analytical framework and contrast how content features differ with sociocultural background in discussions of the emerging COVID-19 global health crisis on major social media platforms. METHODS: We sampled the 1000 most shared viral Twitter and Sina Weibo posts regarding COVID-19, developed a comprehensive coding scheme to identify 77 potential features across six major categories (eg, clinical and epidemiological, countermeasures, politics and policy, responses), quantified feature values (0 or 1, indicating whether or not the content feature is mentioned in the post) in each viral post across social media platforms, and performed subsequent comparative analyses. Machine learning dimension reduction and clustering analysis were then applied to harness the power of social media data and provide more unbiased characterization of web-based health communications. RESULTS: There were substantially different distributions, prevalence, and associations of content features in public discourse about the COVID-19 pandemic on the two social media platforms. Weibo users were more likely to focus on the disease itself and health aspects, while Twitter users engaged more about policy, politics, and other societal issues. CONCLUSIONS: We extracted a rich set of content features from social media data to accurately characterize public discourse related to COVID-19 in different sociocultural backgrounds. In addition, this universal framework can be adopted to analyze social media discussions of other emerging health issues beyond the COVID-19 pandemic.

Are you passing along something true or false? Dissemination of social media messages about genetically modified organisms.

Using network analysis, this study investigates how information veracity and account verification influence the dissemination of information in the context of discourse about genetically modified organisms on social media. We discovered that misinformation and true information about genetically modified organisms demonstrated different dissemination patterns on social media. In general, the dissemination networks of misinformation about genetically modified organisms were found to have higher structural stability than those of true information about genetically modified organisms, as shown by the denser network structure with fewer distinct subgroups residing within the dissemination networks. More importantly, unverified account status significantly boosted the dissemination of misinformation by increasing network density. In addition, we found that the posts about genetically modified organisms from unverified accounts received more reposts and had more layers of information relay than those from the verified accounts. Theoretical and practical implications of these findings on combating misinformation are discussed in the article.

The "Parallel Pandemic" in the Context of China: The Spread of Rumors and Rumor-Corrections During COVID-19 in Chinese Social Media.

Although studies have investigated cyber-rumoring previous to the pandemic, little research has been undertaken to study rumors and rumor-corrections during the COVID-19 (coronavirus disease 2019) pandemic. Drawing on prior studies about how online stories become viral, this study will fill that gap by investigating the retransmission of COVID-19 rumors and corrective messages on Sina Weibo, the largest and most popular microblogging site in China. This study examines the impact of rumor types, content attributes (including frames, emotion, and rationality), and source characteristics (including follower size and source identity) to show how they affect the likelihood of a COVID-19 rumor and its correction being shared. By exploring the retransmission of rumors and their corrections in Chinese social media, this study will not only advance scholarly understanding but also reveal how corrective messages can be crafted to debunk cyber-rumors in particular cultural contexts.