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Can the Content of Social Networks Explain Epidemic Outbreaks?

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Alexandre Gori Maia at State University of Campinas (UNICAMP)
Alexandre Gori Maia
Jose Daniel Morales Martinez
Jose Daniel Morales Martinez
Jose Daniel Morales Martinez
  • This person is not on ResearchGate, or hasn't claimed this research yet.
Leticia Junqueira Marteleto
Leticia Junqueira Marteleto
Leticia Junqueira Marteleto
  • This person is not on ResearchGate, or hasn't claimed this research yet.
Cristina Guimarães Rodrigues at CGR Social Consulting
Cristina Guimarães Rodrigues
  • CGR Social Consulting
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Abstract and Figures

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.
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Vol.:(0123456789)
Population Research and Policy Review (2023) 42:9
https://doi.org/10.1007/s11113-023-09753-7
1 3
RESEARCH BRIEFS
Can theContent ofSocial Networks Explain Epidemic
Outbreaks?
AlexandreGoriMaia1 · JoseDanielMoralesMartinez1 ·
LeticiaJunqueiraMarteleto2 · CristinaGuimaraesRodrigues3·
LuizGustavoSereno1
Received: 2 November 2021 / Accepted: 16 December 2022 / Published online: 10 February 2023
© The Author(s), under exclusive licence to Springer Nature B.V. 2023
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, vac-
cination, health, to politics. We then tested different specifications of spatial econo-
metrics models to relate the frequency of selected keywords with administrative data
on COVID-19 cases and deaths. Our results highlight how mentions of selected key-
words 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
Introduction
In the absence of effective pharmacological treatments, several non-pharmacolog-
ical interventions (NPIs) were adopted to contain the COVID-19 pandemic world-
wide (Iezadi etal., 2020). These measures included, for example, the isolation of
cases, cleaning, use of masks, social distancing, the closing of public and commer-
cial establishments, prohibition of agglomerations, and restrictions on the movement
* Alexandre Gori Maia
gori@unicamp.br
1 University ofCampinas, Campinas, SP, Brazil
2 University ofTexas, Austin, TX, USA
3 The Institute ofEconomic Research Foundation, University ofSão Paulo, SãoPaulo, SP, Brazil
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
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