Can people hear others' crying?: A computational analysis of help-seeking on Weibo during COVID-19 outbreak in China. Issue 5 (September 2022)

• Record Type: Journal Article
• Authors: Zhou, Baohua; Miao, Rong; Jiang, Danting; Zhang, Lingyun
• Is Part Of: Information processing & management. Volume 59:Issue 5(2022)
• Publication Date: 2022-09
• Subjects: COVID-19 -- Help-seeking -- Social media -- Computational communication research; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2022.102997
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: The number of help-seeking posts could be a predictor of pandemic development. Not all help-seeking posts on social media ask for hospitalization. The diffusion and assistance of help-seeking posts are influenced by 3C factors. Retweet mediates the relationship between 3C factors and assistance. Abstract: 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.