Network Structure and Community Evolution Online

Behavioral and Emotional Changes in Response to Covid-19

Datos Bibliográficos

ID22074705
AutoresFan Fang (0000-0002-0402-171X, National University of Defense Technology), Tong Wang (0000-0002-9483-0050, National University of Defense Technology), Suoyi Tan (0000-0002-9108-2229, National University of Defense Technology), Saran Chen (Foshan University), Tao Zhou (0000-0003-1295-8331, University of Electronic Science and Technology of China), Wei Zhang (0000-0002-5458-9581, West China Medical Center of Sichuan University), Qiang Guo (0000-0001-8123-2636, University of Shanghai for Science and Technology), Jianguo Liu (0000-0001-6344-0087), Jian-Guo Liu (0000-0002-5641-0266, Shanghai University of Finance and Economics, autor de correspondencia), Petter Holme (0000-0003-2156-1096, Tokyo Institute of Technology, autor de correspondencia), Xin Lu (0000-0001-6381-3672, National University of Defense Technology, autor de correspondencia)
Año2022
Volumen9
Páginas813234-813234
Fecha de publicación2022-01-11
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaFrontiers in Public Health (JOURNAL)
Identificadores de la revistaISSN: 2296-2565 • E-ISSN: 2296-2565
EditorialFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2021.813234
PMID35087790
OpenAlexW4206231526
IdiomaEN
Citas recibidas2
Referencias citadas64

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

Pandemic · Sentiment analysis · Social media · Social network analysis · Transitive relation · World Wide Web · Complex Network Analysis Techniques · Computer Science · Mathematics · Media Influence and Health · Medicine · Misinformation and Its Impacts · Psychology · Artificial Intelligence