Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/86653
Título: | A multilayered graph-based framework to explore behavioural phenomena in social media conversations |
Autor(es): | Blanco, Guillermo Lourenço, Anália Maria Garcia |
Palavras-chave: | Social media Stance Biased assimilation Homophily Multidimensional analysis |
Data: | Nov-2023 |
Editora: | Elsevier 1 |
Revista: | International Journal of Medical Informatics |
Citação: | Blanco, Guillermo; Lourenço, Anália, A multilayered graph-based framework to explore behavioural phenomena in social media conversations. International Journal of Medical Informatics, 179(105236), 2023 |
Resumo(s): | Objective Social media is part of current health communications. This research aims to delve into the effects of social contagion, biased assimilation, and homophily in building and changing health opinions on social media. Materials and Methods Conversations about COVID-19 vaccination on English and Spanish Twitter are the case studies. A new multilayered graph-based framework supports the integrated analysis of content similarity within and across posts, users, and conversations to interpret contrasting and confluent user stances. Deep learning models are applied to infer stance. Graph centrality and homophily scores support the interpretation of information reproduction. Results The results show that semantically related English posts tend to present a similar stance about COVID-19 vaccination (rstance=0.51) whereas Spanish posts are more heterophilic (rstance=0.38). Neither case showed evidence of homophily regarding user influence or vaccine hashtags. Graph filters for Pfizer and Astrazeneca with a similarity threshold of 0.85 show stance homophily in English scenarios (i.e. rstance=0.45 and rstance=0.58, respectively) and small homophily in Spanish scenarios (i.e. r=0.12 and r=0.3, respectively). Highly connected users are a minority and are not socially influential. Spanish conversations showed stance homophily, i.e. most of the connected conversations promote vaccination (rstance=0.42), whereas English conversations are more likely to offer contrasting stances. Conclusion The methodology proposed for quantifying the impact of natural and intentional social behaviours in health information reproduction can be applied to any of the main social platforms and any given topic of conversation. Its effectiveness was demonstrated by two case studies describing English and Spanish demographic and sociocultural scenarios. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/86653 |
DOI: | 10.1016/j.ijmedinf.2023.105236 |
ISSN: | 1386-5056 |
Versão da editora: | https://www.sciencedirect.com/journal/international-journal-of-medical-informatics |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | CEB - Publicações em Revistas/Séries Internacionais / Publications in International Journals/Series |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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document_56424_1.pdf | 3,06 MB | Adobe PDF | Ver/Abrir |