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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| Accès en ligne: | https://arxiv.org/abs/2401.06255 |
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| _version_ | 1866911781060870144 |
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| author | Han, Yueting Bazzi, Marya Turrini, Paolo |
| author_facet | Han, Yueting Bazzi, Marya Turrini, Paolo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_06255 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Modelling and Predicting Online Vaccination Views using Bow-tie Decomposition Han, Yueting Bazzi, Marya Turrini, Paolo Social and Information Networks 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. |
| title | Modelling and Predicting Online Vaccination Views using Bow-tie Decomposition |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2401.06255 |