Monitoring Real-time Spatial Public Health Discussions in the Context of Vaccine Hesitancy

Social media provide the potential to keep up with public discussions more quickly, at lower cost, and at potentially higher granularity and scope than do traditional surveys. This paper details a preliminary system of real-time geographical monitoring and analysis using the context of the vaccine-hesitancy discussion across the United States, a valuable backdrop for such a system because of the diverse and impactful nature of the vaccination discussions as they appear, change, and influence the public. We combine various methods in machine learning to geolocate, categorize, and classify vaccination discussions on Twitter. As a proof of concept, we show analyses with a prominent anti-vaccine discussion that validate the system with results from traditional surveys, yet also provide valuable spatial statistical power on top of such surveys on maps of the United States. We detail limitations and future work, yet still conclude that the system and the answers it enables are important because they will allow for more targeted and effective communication and reaction to the discussion as a first step towards monitoring people’s views.

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