A multilayered graph-based framework to explore behavioural phenomena in social media conversations.

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. 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. The results show that semantically related English posts tend to present a similar stance about COVID-19 vaccination (r 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.

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Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.