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

Guillermo Blanco1, Anália Lourenço2

  • 1Universidade de Vigo, Department of Computer Science, ESEI-Escuela Superior de Ingeniería Informática, Edificio Politécnico, Campus Universitario As Lagoas s/n, 32004 Ourense, Spain; CINBIO, The Biomedical Research Centre, Universidade de Vigo, Campus Univesitario Lagoas-Marcosende, 36310 Vigo, Spain; SING, Next Generation Computer Systems Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain.

International Journal of Medical Informatics
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PubMed
Summary

Social media influences health opinions through social contagion and homophily. This study analyzed COVID-19 vaccination conversations on Twitter, finding language differences in opinion sharing and limited user influence.

Keywords:
Biased assimilationHomophilyMultidimensional analysisSocial mediaStance

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Area of Science:

  • Social media analysis
  • Health communication
  • Computational social science

Background:

  • Social media platforms are integral to modern health communication.
  • Understanding the dynamics of health opinion formation on these platforms is crucial.

Purpose of the Study:

  • To investigate the influence of social contagion, biased assimilation, and homophily on health opinions shared on social media.
  • To analyze how these factors shape and alter user stances regarding health topics, using COVID-19 vaccination as a case study.

Main Methods:

  • Utilized a multilayered graph-based framework to analyze English and Spanish Twitter conversations about COVID-19 vaccination.
  • Applied deep learning models to infer user stances and employed graph centrality and homophily scores to interpret information reproduction patterns.

Main Results:

  • English posts showed higher stance similarity (r=0.51) compared to Spanish posts (r=0.38).
  • Stance homophily was observed for specific vaccines (Pfizer, AstraZeneca) in English and to a lesser extent in Spanish conversations.
  • Highly connected users were a minority and lacked significant social influence, while Spanish conversations predominantly promoted vaccination, unlike English ones which presented more contrasting views.

Conclusions:

  • The developed methodology effectively quantifies social behaviors' impact on health information dissemination across social platforms.
  • Demonstrated effectiveness through case studies in English and Spanish, highlighting demographic and sociocultural differences in online health discourse.