Influence of content and creator characteristics on sharing disaster-related information on social media

Mapping the Global Evolution and Research Directions of Information Seeking, Sharing and Communication in Disasters: A Bibliometric Study

This paper aims to grasp developments and trends in research on information communication, information seeking and information sharing in disasters during 2000-2021. By using bibliometrics software CiteSpace and VOSviewer, the development trends of publications, disciplinary, journals, institutions and regional cooperation are mapped. Keyword co-occurrence analysis is used to further identify the evolution of the research hot points and visualize the research orientation and frontier. The results indicate that the field of information communication in disasters has received growing attention from various disciplines. Results of institutions and regional cooperation show that worldwide cooperation is still lacking and needs to be strengthened in future studies in this field. The key findings are five main research orientations in this field based on keyword co-occurrence, which are public information coordination research, public information behavior and perception research, health information communication research, risk communication and social media research and information technology in emergency management. The findings of this paper can be helpful for academics and emergency managers in disaster information management and risk communication by giving them a comprehensive understanding of people's information communication, seeking and sharing.

Can people hear others' crying?: A computational analysis of help-seeking on Weibo during COVID-19 outbreak in China

Social media like Weibo has become an important platform for people to ask for help during COVID-19 pandemic. Using a complete dataset of help-seeking posts on Weibo during the COVID-19 outbreak in China (N = 3,705,188), this study mapped their characteristics and analyzed their relationship with the epidemic development at the aggregate level, and examined the influential factors to determine whether and the extent the help-seeking crying could be heard at the individual level using computational methods for the first time. It finds that the number of help-seeking posts on Weibo has a Granger causality relationship with the number of confirmed COVID-19 cases with a time lag of eight days. This study then proposes a 3C framework to examine the direct influence of content, context, and connection on the responses (measured by retweets and comments) and assistance that help-seekers might receive as well as their indirect effects on assistance through the mediation of both retweets and comments. The differential influences of content (theme and negative sentiment), context (Super topic community, spatial location of posting, and the period of sending time), and connection (the number of followers, whether mentioning others, and verified status of authors and sharers) have been reported and discussed.

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

Background

During global health crises such as the COVID-19 pandemic, rapid spread of misinformation on social media has occurred. The misinformation associated with COVID-19 has been analyzed, but little attention has been paid to developing a comprehensive analytical framework to study its spread on social media.

Objective

We propose an elaboration likelihood model–based theoretical model to understand the persuasion process of COVID-19–related misinformation on social media.

Methods

The proposed model incorporates the central route feature (content feature) and peripheral features (including creator authority, social proof, and emotion). The central-level COVID-19–related misinformation feature includes five topics: medical information, social issues and people’s livelihoods, government response, epidemic spread, and international issues. First, we created a data set of COVID-19 pandemic–related misinformation based on fact-checking sources and a data set of posts that contained this misinformation on real-world social media. Based on the collected posts, we analyzed the dissemination patterns.

Results

Our data set included 11,450 misinformation posts, with medical misinformation as the largest category (n=5359, 46.80%). Moreover, the results suggest that both the least (4660/11,301, 41.24%) and most (2320/11,301, 20.53%) active users are prone to sharing misinformation. Further, posts related to international topics that have the greatest chance of producing a profound and lasting impact on social media exhibited the highest distribution depth (maximum depth=14) and width (maximum width=2355). Additionally, 97.00% (2364/2437) of the spread was characterized by radiation dissemination.

Conclusions

Our proposed model and findings could help to combat the spread of misinformation by detecting suspicious users and identifying propagation characteristics.