Quantifying COVID-19 Content in the Online Health Opinion War Using Machine Learning
Richard F Sear1, Nicolas Velasquez2,3, Rhys Leahy2,4
1Department of Computer ScienceGeorge Washington UniversityWashingtonDC20052USA.
IEEE Access : Practical Innovations, Open Solutions
|June 30, 2021
Summary
The anti-vaccination community discusses COVID-19 more broadly than the pro-vaccination community, potentially attracting more support. This poses a risk to achieving herd immunity and controlling COVID-19 spread.
Area of Science:
- Online Health Misinformation
- Computational Social Science
- Public Health Communication
Background:
- COVID-19 misinformation poses a significant threat to public health.
- Online communities, particularly "anti-vax" and "pro-vax" groups, shape public discourse on vaccines and health guidance.
- Understanding the dynamics of these online communities is crucial for effective public health interventions.
Purpose of the Study:
- To quantify and analyze COVID-19 related content within online anti-vaccination communities.
- To compare the discourse patterns of anti-vaccination and pro-vaccination communities regarding COVID-19.
- To assess the potential of anti-vaccination communities to attract new adherents.
Main Methods:
- Utilized machine learning techniques to analyze large volumes of online content.
- Quantified COVID-19 discussion topics within identified online communities.
- Developed a mechanistic model to interpret findings and evaluate intervention strategies.
Main Results:
- The anti-vaccination community exhibits a broader range of COVID-19 topics compared to the more focused pro-vaccination community.
- Anti-vaccination discourse encompasses diverse themes, appealing to individuals wary of vaccines or seeking alternative health information.
- The anti-vaccination community appears better positioned to gain support, posing a risk to COVID-19 vaccine adoption and herd immunity.
Conclusions:
- The broader appeal of anti-vaccination content could hinder global efforts to achieve herd immunity.
- Machine learning offers a scalable solution for analyzing health misinformation on social media.
- Findings can inform the development of targeted strategies to combat health misinformation and promote vaccine uptake.
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