Accounting for social media effects to improve the accuracy of infection models: combatting the COVID-19 pandemic and infodemic

Sujin Bae, Eunyoung Sung, Ohbyung Kwon

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


During the COVID-19 pandemic, social media platforms such as Twitter, Facebook, etc. have played an important role in conveying information, both accurate and inaccurate, thereby creating mass confusion. As the response to COVID-19 has reduced face-to-face contact, communication via social media has increased. Evidence shows that social media affects disease (non-)prevention through the (im)proper distribution of information, and distorts the predictive accuracy of infection models, including legacy Susceptible–Exposed–Infectious–Recovered (SEIR) models. Our adjusted SEIR model reflects the effectiveness of information disseminated through social media by accounting for dimensions of social/informational motivation based on social learning/use and gratification theories, and uses Monte Carlo methodology and computational algorithms to predict effects of social media on the spread of COVID-19 (N = 2,095 cases). The results suggest that social media utilisation measures should be incorporated into SEIR models to improve forecasts of COVID-19 infections. Utilising IS to analyse the spread of digital information via social media platforms can inform efforts to combat the pandemic and infodemic. Agencies responsible for infection and disease control, policy makers, businesses, institutions and educators must accurately monitor infection rates to appropriately allocate funding and human resources and develop effective disease prevention marketing campaigns.

Original languageEnglish
Pages (from-to)342-355
Number of pages14
JournalEuropean Journal of Information Systems
Issue number3
Publication statusPublished - 2021


  • COVID-19 infection
  • Pär Ågerfalk, Kieran Conboy and Michael Myers
  • SEIR model
  • big data analysis
  • monte carlo method
  • social media


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