ArticleInformation processing & management2022
Investigating the COVID-19 vaccine discussions on Twitter through a multilayer network-based approach.
Article in Information processing & management, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study.Journal of medical Internet research · 2024Article
- Utilizing AI for extracting insights on post WHO's COVID-19 vaccination declaration from X (Twitter) social network.AIMS public health · 2024Article
- Unveiling Vaccine Hesitancy on Twitter: Analyzing Trends and Reasons during the Emergence of COVID-19 Delta and Omicron Variants.Vaccines · 2023Article
- COVID-19 Testing, Vaccine Perceptions, and Trust among Hispanics Residing in an Underserved Community.International journal of environmental research and public health · 2023Article
- Adolescent Distress: Is There a Vaccine? Social and Cultural Considerations during the COVID-19 Pandemic.International journal of environmental research and public health · 2023Article
- Impact of COVID-19 Pandemic on Mental Health and Socioeconomic Aspects in Greece.International journal of environmental research and public health · 2023Article
- Deep learning-based credibility conversation detection approaches from social network.Social network analysis and mining · 2023Article
- Anti-Vaccine Discourse on Social Media: An Exploratory Audit of Negative Tweets about Vaccines and Their Posters.Vaccines · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Modeling discussions on social networks is a challenging task, especially if we consider sensitive topics, such as politics or healthcare. However, the knowledge hidden in these debates helps to investigate trends and opinions and to identify the cohesion of users when they deal with a specific topic. To this end, we propose a general multilayer network approach to investigate discussions on a social network. In order to prove the validity of our model, we apply it on a Twitter dataset containing tweets concerning opinions on COVID-19 vaccines. We extract a set of relevant hashtags (i.e., gold-standard hashtags) for each line of thought (i.e., pro-vaxxer, neutral, and anti-vaxxer). Then, thanks to our multilayer network model, we figure out that the anti-vaxxers tend to have ego networks denser (+14.39%) and more cohesive (+64.2%) than the ones of pro-vaxxer, which leads to a higher number of interactions among anti-vaxxers than pro-vaxxers (+393.89%). Finally, we report a comparison between our approach and one based on single networks analysis. We prove the effectiveness of our model to extract influencers having ego networks with more nodes (+40.46%), edges (+39.36%), and interactions with their neighbors (+28.56%) with respect to the other approach. As a result, these influential users are much more important to analyze and can provide more valuable information.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.