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Network Structure and Community Evolution Online: Behavioral and Emotional Changes in Response to COVID-19
Fan Fang1, Tong Wang1, Suoyi Tan1
1College of Systems Engineering, National University of Defense Technology, Changsha, China.
Frontiers in Public Health
|January 28, 2022
Summary
Social media analysis of Wuhan
Area of Science:
- Social Network Analysis
- Computational Social Science
- Epidemiology
Background:
- Understanding social structure changes during pandemics like COVID-19 is crucial for public response.
- Exceptional events offer insights into collective social processes.
Purpose of the Study:
- To develop a framework using social media data to analyze public behavioral and emotional shifts during COVID-19.
- To investigate the evolution of social networks and user behavior in response to the pandemic.
Main Methods:
- Utilized Sina Weibo data from Wuhan (Dec 2019-Mar 2020) for 3.5 million users.
- Employed community detection, text analysis, and sentiment analysis on a time-varying social network.
- Analyzed social network structure, behavior, and emotions across four epidemic stages.
Main Results:
- Network indicators (size, interaction frequency) increased during the outbreak.
- Topic similarity rose, with increased discussion of COVID-19.
- COVID-19-focused communities grew by 40%; a significant emotional "rebound effect" was observed.
Conclusions:
- Network and community structure evolution explains collective social process changes during pandemics.
- Provides data-driven insights into public attention dynamics during extreme events.
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