Can the Content of Social Networks Explain Epidemic Outbreaks?

Alexandre Gori Maia (), Jose Daniel Morales Martinez, Leticia Junqueira Marteleto, Cristina Guimaraes Rodrigues and Luiz Gustavo Sereno
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Alexandre Gori Maia: University of Campinas
Jose Daniel Morales Martinez: University of Campinas
Leticia Junqueira Marteleto: University of Texas
Cristina Guimaraes Rodrigues: University of São Paulo
Luiz Gustavo Sereno: University of Campinas

Population Research and Policy Review, 2023, vol. 42, issue 1, No 1, 34 pages

Abstract: Abstract 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.

Keywords: Twitter; COVID-19; Spatial models; Social behavior; Public health; Spatial panel model (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11113-023-09753-7

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