Quantifying COVID-19 Content in the Online Health Opinion War Using Machine Learning

Abstract

A huge amount of potentially dangerous COVID-19 misinformation is appearing online. Here we use machine learning to quantify COVID-19 content among online opponents of establishment health guidance, in particular vaccinations (''anti-vax''). We find that the anti-vax community is developing a less focused debate around COVID-19 than its counterpart, the pro-vaccination (''pro-vax'') community. However, the anti-vax community exhibits a broader range of ''flavors'' of COVID-19 topics, and hence can appeal to a broader cross-section of individuals seeking COVID-19 guidance online, e.g. individuals wary of a mandatory fast-tracked COVID-19 vaccine or those seeking alternative remedies. Hence the anti-vax community looks better positioned to attract fresh support going forward than the pro-vax community. This is concerning since a widespread lack of adoption of a COVID-19 vaccine will mean the world falls short of providing herd immunity, leaving countries open to future COVID-19 resurgences. We provide a mechanistic model that interprets these results and could help in assessing the likely efficacy of intervention strategies. Our approach is scalable and hence tackles the urgent problem facing social media platforms of having to analyze huge volumes of online health misinformation and disinformation. INDEX TERMS COVID-19, machine learning, topic modeling, mechanistic model, social computing.

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