ACADEMIC ARTICLE

Alexandre Gori Maia (IE/UNICAMP); Jose Daniel Morales Martinez (IE/UNICAMP); Leticia Junqueira Marteleto (UT/Austin); Cristina Guimaraes Rodrigues (FIPE/USP); Luiz Gustavo Sereno (IE/UNICAMP) | Population Research and Policy Review 

People share and seek information online that reflects a variety of social phenomena, including concerns about health conditions. We analyze how the contents of social networks provide real-time information to monitor and anticipate policies aimed at controlling or mitigating public health outbreaks. In November 2020, we collected tweets on the COVID-19 pandemic with content ranging from safety measures, vaccination, health, to politics. We then tested different specifications of spatial econometrics models to relate the frequency of selected keywords with administrative data on COVID-19 cases and deaths. Our results highlight how mentions of selected keywords can significantly explain future COVID-19 cases and deaths in one locality. We discuss two main mechanisms potentially explaining the links we find between Twitter contents and COVID-19 diffusion: risk perception and health behavior. This research was funded by the Ministry of Health and the National Council for Scientific and Technological Development in Brazil.

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