Combatting Mis/Disinformation: Combining Predictive Modeling and Machine Learning with Persuasion Science to Understand COVID-19 Vaccine Online Discourse

Main Article Content

Denise Scannell,, Ph.D. Linda Desens, Ph.D. David S.Day, Ph.D. Yolande Tra, Ph.D.

Abstract

Health mis/disinformation can negatively impact health decisions and ultimately, health outcomes. Mis/disinformation related to COVID-19 vaccines has influenced vaccine hesitancy during a very critical time during the pandemic when globally, the vaccine was needed to attenuate the spread of the COVID-19 virus. This paper examines persuasive strategies used in Twitter posts, particular those with antivaccine sentiment. The authors developed a predictive model using variables based on the Elaboration Likelihood Model, Social Judgement Theory and the Extended Parallel Process Model to determine which persuasive tactics resulted in antivaccine, provaccine and neutral sentiment. The study also used machine learning to validate the persuasion variable algorithm to detect persuasion tactics in COVID-19 vaccine online discourse on Twitter. Understanding persuasive tactics used in antivaccine messaging can inform the development of a data-driven counter-response strategy.


Key words: Misinformation, Disinformation, Persuasion, Algorithm, Sentiment

Article Details

How to Cite
SCANNELL,, Denise et al. Combatting Mis/Disinformation: Combining Predictive Modeling and Machine Learning with Persuasion Science to Understand COVID-19 Vaccine Online Discourse. Medical Research Archives, [S.l.], v. 10, n. 5, june 2022. ISSN 2375-1924. Available at: <https://esmed.org/MRA/mra/article/view/2822>. Date accessed: 30 june 2022. doi: https://doi.org/10.18103/mra.v10i5.2822.
Section
Research Articles

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