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Investigating the COVID-19 vaccine discussions on Twitter through a multilayer network-based approach
Gianluca Bonifazi1, Bernardo Breve2, Stefano Cirillo2
1DII, Polytechnic University of Marche, Italy.
Information Processing & Management
|September 19, 2022
Summary
Analyzing social media discussions on sensitive topics like COVID-19 vaccines reveals distinct user behaviors. Anti-vaccine groups exhibit denser, more cohesive networks, indicating higher interaction rates compared to pro-vaccine communities.
Area of Science:
- Social Network Analysis
- Computational Social Science
- Public Health Informatics
Background:
- Modeling discussions on social networks is complex, particularly for sensitive topics like politics and healthcare.
- Understanding user cohesion and trends within online debates is crucial for identifying public opinion and information dissemination patterns.
Purpose of the Study:
- To propose a general multilayer network approach for investigating social network discussions.
- To analyze user behavior and network structures within the context of COVID-19 vaccine opinions on Twitter.
Main Methods:
- Development of a general multilayer network model.
- Application of the model to a Twitter dataset of COVID-19 vaccine discussions.
- Extraction of gold-standard hashtags for pro-vaxxer, neutral, and anti-vaxxer viewpoints.
- Comparative analysis against single network approaches.
Main Results:
- Anti-vaxxer ego networks are denser (+14.39%) and more cohesive (+64.2%) than pro-vaxxer networks.
- Anti-vaxxers demonstrate significantly higher interaction rates (+393.89%) within their networks.
- The multilayer network model effectively identifies influencers with larger ego networks and more neighbor interactions compared to single network analysis.
Conclusions:
- Multilayer network analysis provides a more effective framework for understanding complex social media discussions, especially on sensitive health topics.
- Anti-vaccine communities exhibit distinct network characteristics, suggesting potential differences in information propagation and user engagement.
- Identifying influential users through this advanced modeling approach is critical for deeper insights into public discourse.
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