Modelling and predicting online vaccination views using bow-tie decomposition
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-vaccinati...
| Главные авторы: | , , |
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| Формат: | Статья |
| Язык: | English |
| Опубликовано: |
The Royal Society
2024-02-01
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| Серии: | Royal Society Open Science |
| Предметы: | |
| Online-ссылка: | https://royalsocietypublishing.org/doi/10.1098/rsos.231792 |
| _version_ | 1827346321490575360 |
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| author | Yueting Han Marya Bazzi Paolo Turrini |
| author_facet | Yueting Han Marya Bazzi Paolo Turrini |
| author_sort | Yueting Han |
| collection | DOAJ |
| description | 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, strongly connected component (SCC) and out-periphery component (OUT), emphasized 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. |
| first_indexed | 2024-03-07T23:28:23Z |
| format | Article |
| id | doaj.art-21cd49f3e5d547c9aa7a64567db2ab8c |
| institution | Directory Open Access Journal |
| issn | 2054-5703 |
| language | English |
| last_indexed | 2024-03-07T23:28:23Z |
| publishDate | 2024-02-01 |
| publisher | The Royal Society |
| record_format | Article |
| series | Royal Society Open Science |
| spelling | doaj.art-21cd49f3e5d547c9aa7a64567db2ab8c2024-02-21T00:05:20ZengThe Royal SocietyRoyal Society Open Science2054-57032024-02-0111210.1098/rsos.231792Modelling and predicting online vaccination views using bow-tie decompositionYueting Han0Marya Bazzi1Paolo Turrini2MathSys CDT, University of Warwick, Coventry, UKMathematics Institute, University of Warwick, Coventry, UKDepartment of Computer Science, University of Warwick, Coventry, UKSocial 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, strongly connected component (SCC) and out-periphery component (OUT), emphasized 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.https://royalsocietypublishing.org/doi/10.1098/rsos.231792computational social sciencesocial networksdata analysisopinion dynamicssocial psychology |
| spellingShingle | Yueting Han Marya Bazzi Paolo Turrini Modelling and predicting online vaccination views using bow-tie decomposition Royal Society Open Science computational social science social networks data analysis opinion dynamics social psychology |
| title | Modelling and predicting online vaccination views using bow-tie decomposition |
| title_full | Modelling and predicting online vaccination views using bow-tie decomposition |
| title_fullStr | Modelling and predicting online vaccination views using bow-tie decomposition |
| title_full_unstemmed | Modelling and predicting online vaccination views using bow-tie decomposition |
| title_short | Modelling and predicting online vaccination views using bow-tie decomposition |
| title_sort | modelling and predicting online vaccination views using bow tie decomposition |
| topic | computational social science social networks data analysis opinion dynamics social psychology |
| url | https://royalsocietypublishing.org/doi/10.1098/rsos.231792 |
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