| Texto completo | |
| Autor(es): |
Maia, Alexandre Gori
;
Martinez, Jose Daniel Morales
;
Marteleto, Leticia Junqueira
;
Rodrigues, Cristina Guimaraes
;
Sereno, Luiz Gustavo
Número total de Autores: 5
|
| Tipo de documento: | Artigo Científico |
| Fonte: | POPULATION RESEARCH AND POLICY REVIEW; v. 42, n. 1, p. 34-pg., 2023-02-01. |
| Resumo | |
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. (AU) | |
| Processo FAPESP: | 20/09838-0 - BI0S - Brazilian Institute of Data Science |
| Beneficiário: | João Marcos Travassos Romano |
| Modalidade de apoio: | Auxílio à Pesquisa - Programa Centros de Pesquisa em Engenharia |