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Modelling and predicting online vaccination views using bow-tie decomposition
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Han, Yueting, Bazzi, Marya and Turrini, Paolo (2024) Modelling and predicting online vaccination views using bow-tie decomposition. Royal Society Open Science, 11 (2). 231792. doi:10.1098/rsos.231792 ISSN 2054-5703.
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Official URL: https://doi.org/10.1098/rsos.231792
Abstract
Social media has become increasingly important in shaping public vaccination views, especially since the COVID-19 outbreak. This paper uses bow-tie structure to analyse a temporal dataset of directed online social networks that represent the information exchange among anti-vaccination, pro-vaccination, and neutral Facebook pages. Bow-tie structure decomposes a network into seven components, with two components “SCC” and “OUT” emphasised in this paper: SCC is the largest strongly connected component, acting as an “information magnifier”, and OUT contains all nodes with a directed path from a node in SCC, acting as an “information creator”. We consistently observe statistically significant bow-tie structures with different dominant components for each vaccination group over time. In particular, the anti-vaccination group has a large OUT, and the pro-vaccination group has a large SCC. We further investigate changes in opinions over time, as measured by fan count variations, using agent-based simulations and machine learning models. Across both methods, accounting for bow-tie decomposition better reflects information flow differences among vaccination groups and improves our opinion dynamics prediction results. The modelling frameworks we consider can be applied to any multi-stance temporal network and could form a basis for exploring opinion dynamics using bow-tie structure in a wide range of applications.
Item Type: | Journal Article | |||||||||
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Subjects: | Q Science > QA Mathematics | |||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Research Centres > Centre for Complexity Science Faculty of Science, Engineering and Medicine > Science > Computer Science Faculty of Science, Engineering and Medicine > Science > Mathematics |
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Library of Congress Subject Headings (LCSH): | Social media -- Data processing, Sentiment analysis, Vaccination -- Public opinion -- Computer simulation, Vaccination -- Public opinion -- Data processing | |||||||||
Journal or Publication Title: | Royal Society Open Science | |||||||||
Publisher: | The Royal Society Publishing | |||||||||
ISSN: | 2054-5703 | |||||||||
Official Date: | February 2024 | |||||||||
Dates: |
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Volume: | 11 | |||||||||
Number: | 2 | |||||||||
Article Number: | 231792 | |||||||||
DOI: | 10.1098/rsos.231792 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||
Date of first compliant deposit: | 24 January 2024 | |||||||||
Date of first compliant Open Access: | 21 February 2024 | |||||||||
RIOXX Funder/Project Grant: |
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