Abstract
Background: The measurement and identification of changes in the social structure in response to an exceptional event like COVID-19 can facilitate a more informed public response to the pandemic and provide fundamental insights on how collective social processes respond to extreme events.Objective: In this study, we built a generalized framework for applying social media data to understand public behavioral and emotional changes in response to COVID-19.Methods: Utilizing a complete dataset of Sina Weibo posts published by users in Wuhan from December 2019 to March 2020, we constructed a time-varying social network of 3.5 million users. In combination with community detection, text analysis, and sentiment analysis, we comprehensively analyzed the evolution of the social network structure, as well as the behavioral and emotional changes across four main stages of Wuhan's experience with the epidemic.Results: The empirical results indicate that almost all network indicators related to the network's size and the frequency of social interactions increased during the outbreak. The number of unique recipients, average degree, and transitivity increased by 24, 23, and 19% during the severe stage than before the outbreak, respectively. Additionally, the similarity of topics discussed on Weibo increased during the local peak of the epidemic. Most people began discussing the epidemic instead of the more varied cultural topics that dominated early conversations. The number of communities focused on COVID-19 increased by nearly 40 percent of the total number of communities. Finally, we find a statistically significant “rebound effect” by exploring the emotional content of the users' posts through paired sample t-test (P = 0.003).Conclusions: Following the evolution of the network and community structure can explain how collective social processes changed during the pandemic. These results can provide data-driven insights into the development of public attention during extreme events.
Highlights
Understanding how social structure and public attention evolve throughout a pandemic can support public policy that is more in line with local understanding and expectations
Frequency of Epidemic-Related Keywords Microblogs about COVID-19 were filtered from the full dataset based on a prior set of keywords related to the epidemic [39, 40]
Our study built a generalized framework for incorporating social media data into understanding the rise and fell of public attention to COVID-19 with a complete dataset of Sina Weibo published in Wuhan
Summary
Understanding how social structure and public attention evolve throughout a pandemic can support public policy that is more in line with local understanding and expectations. This understanding can provide fundamental insights into how collective social processes evolve, influence participants’ emotions, and change behavior and disease outcomes. Research on major societal disturbances can be divided into three categories: mechanism of information dissemination, early warning and prediction of extreme events, and analysis of public behavioral and emotional changes. The measurement and identification of changes in the social structure in response to an exceptional event like COVID-19 can facilitate a more informed public response to the pandemic and provide fundamental insights on how collective social processes respond to extreme events
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