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Tracking the structure and sentiment of vaccination discussions on Mumsnet
Vaccination is one of the most impactful healthcare interventions in terms of lives saved at a given cost, leading the anti-vaccination movement to be identified as one of the top 10 threats to global health in 2019 by the World Health Organization. This issue increased in importance during the COVI...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Vienna
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10657328/ https://www.ncbi.nlm.nih.gov/pubmed/38026264 http://dx.doi.org/10.1007/s13278-023-01155-z |
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author | Silva, Miguel E. P. Skeva, Rigina House, Thomas Jay, Caroline |
author_facet | Silva, Miguel E. P. Skeva, Rigina House, Thomas Jay, Caroline |
author_sort | Silva, Miguel E. P. |
collection | PubMed |
description | Vaccination is one of the most impactful healthcare interventions in terms of lives saved at a given cost, leading the anti-vaccination movement to be identified as one of the top 10 threats to global health in 2019 by the World Health Organization. This issue increased in importance during the COVID-19 pandemic where, despite good overall adherence to vaccination, specific communities still showed high rates of refusal. Online social media has been identified as a breeding ground for anti-vaccination discussions. In this work, we study how vaccination discussions are conducted in the discussion forum of Mumsnet, a UK-based website aimed at parents. By representing vaccination discussions as networks of social interactions, we can apply techniques from network analysis to characterize these discussions, namely network comparison, a task aimed at quantifying similarities and differences between networks. Using network comparison based on graphlets—small connected network subgraphs—we show how the topological structure of vaccination discussions on Mumsnet differs over time, in particular before and after COVID-19. We also perform sentiment analysis on the content of the discussions and show how the sentiment toward vaccinations changes over time. Our results highlight an association between differences in network structure and changes to sentiment, demonstrating how network comparison can be used as a tool to guide and enhance the conclusions from sentiment analysis. |
format | Online Article Text |
id | pubmed-10657328 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Vienna |
record_format | MEDLINE/PubMed |
spelling | pubmed-106573282023-11-18 Tracking the structure and sentiment of vaccination discussions on Mumsnet Silva, Miguel E. P. Skeva, Rigina House, Thomas Jay, Caroline Soc Netw Anal Min Original Article Vaccination is one of the most impactful healthcare interventions in terms of lives saved at a given cost, leading the anti-vaccination movement to be identified as one of the top 10 threats to global health in 2019 by the World Health Organization. This issue increased in importance during the COVID-19 pandemic where, despite good overall adherence to vaccination, specific communities still showed high rates of refusal. Online social media has been identified as a breeding ground for anti-vaccination discussions. In this work, we study how vaccination discussions are conducted in the discussion forum of Mumsnet, a UK-based website aimed at parents. By representing vaccination discussions as networks of social interactions, we can apply techniques from network analysis to characterize these discussions, namely network comparison, a task aimed at quantifying similarities and differences between networks. Using network comparison based on graphlets—small connected network subgraphs—we show how the topological structure of vaccination discussions on Mumsnet differs over time, in particular before and after COVID-19. We also perform sentiment analysis on the content of the discussions and show how the sentiment toward vaccinations changes over time. Our results highlight an association between differences in network structure and changes to sentiment, demonstrating how network comparison can be used as a tool to guide and enhance the conclusions from sentiment analysis. Springer Vienna 2023-11-18 2023 /pmc/articles/PMC10657328/ /pubmed/38026264 http://dx.doi.org/10.1007/s13278-023-01155-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Article Silva, Miguel E. P. Skeva, Rigina House, Thomas Jay, Caroline Tracking the structure and sentiment of vaccination discussions on Mumsnet |
title | Tracking the structure and sentiment of vaccination discussions on Mumsnet |
title_full | Tracking the structure and sentiment of vaccination discussions on Mumsnet |
title_fullStr | Tracking the structure and sentiment of vaccination discussions on Mumsnet |
title_full_unstemmed | Tracking the structure and sentiment of vaccination discussions on Mumsnet |
title_short | Tracking the structure and sentiment of vaccination discussions on Mumsnet |
title_sort | tracking the structure and sentiment of vaccination discussions on mumsnet |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10657328/ https://www.ncbi.nlm.nih.gov/pubmed/38026264 http://dx.doi.org/10.1007/s13278-023-01155-z |
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