Proceedings of the 2021 Conference of The Computational Social Science Society of the Americas
The COVID-19 pandemic sparked an online “infodemic” of potentially dangerous misinformation. We use machine learning to quantify COVID-19 content from opponents of establishment health guidance, in particular vaccination. We quantify this content in two different ways: number of topics and evolution of keywords. We find that, even in the early stages of the pandemic, the anti-vaccination community had the infrastructure to more effectively garner support than their pro-vaccination counterparts by exhibiting a broader array of discussion topics. This provided an advantage in terms of attracting new users seeking COVID-19 guidance online. We also find that our machine learning framework can pick up on the adaptive nature of discussions within the anti-vaccination community, tracking distrust of authorities, opposition to lockdown orders, and an interest in early vaccine trials. Our approach is scalable and hence tackles the urgent problem facing social media platforms of having to analyze huge volumes of online health misinformation. With vaccine booster shots being approved and vaccination rates stagnating, such an automated approach is key in understanding how to combat the misinformation that slows the eradication of the pandemic.
Richard Sear, Rhys Leahy, Nicholas Johnson Restrepo, Yonatan Lupu, Neil Johnson