2026-02-10T09:38:58Z
2026-02-10T09:38:58Z
2020
2026-02-10T09:38:58Z
Background: The exposure and consumption of information during epidemic outbreaks may alter people's risk perception and trigger behavioral changes, which can ultimately affect the evolution of the disease. It is thus of utmost importance to map the dissemination of information by mainstream media outlets and the public response to this information. However, our understanding of this exposure-response dynamic during the COVID-19 pandemic is still limited. Objective: The goal of this study is to characterize the media coverage and collective internet response to the COVID-19 pandemic in four countries: Italy, the United Kingdom, the United States, and Canada. Methods: We collected a heterogeneous data set including 227,768 web-based news articles and 13,448 YouTube videos published by mainstream media outlets, 107,898 user posts and 3,829,309 comments on the social media platform Reddit, and 278,456,892 views of COVID-19'related Wikipedia pages. To analyze the relationship between media coverage, epidemic progression, and users' collective web-based response, we considered a linear regression model that predicts the public response for each country given the amount of news exposure. We also applied topic modelling to the data set using nonnegative matrix factorization. Results: Our results show that public attention, quantified as user activity on Reddit and active searches on Wikipedia pages, is mainly driven by media coverage; meanwhile, this activity declines rapidly while news exposure and COVID-19 incidence remain high. Furthermore, using an unsupervised, dynamic topic modeling approach, we show that while the levels of attention dedicated to different topics by media outlets and internet users are in good accordance, interesting deviations emerge in their temporal patterns. Conclusions: Overall, our findings offer an additional key to interpret public perception and response to the current global health emergency and raise questions about the effects of attention saturation on people's collective awareness and risk perception and thus on their tendencies toward behavioral change.
The authors would like to thank the startup company Quick Algorithm for providing the platform where the data collected during the COVID-19 pandemic were visualized in real time [112]. DP and MT acknowledge support from the Lagrange Project of the Institute for Scientific Interchange Foundation (ISI Foundation) funded by Fondazione Cassa di Risparmio di Torino (Fondazione CRT). MT acknowledges support from EPIPOSE (Epidemic intelligence to minimize COVID-19's public health, societal and economic impact) H2020-SC1-PHE-CORONAVIRUS-2020 call. MS and AP acknowledge support from the Research Project 'Casa Nel Parco' (POR FESR 14/20 - CANP - Cod. 320 - 16 - Piattaforma Tecnologica 'Salute e Benessere') funded by Regione Piemonte in the context of the Regional Platform on Health and Wellbeing. AP acknowledges partial support from Intesa Sanpaolo Innovation Center. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. NG acknowledges support from the Doctoral Training Alliance.
Article
Published version
English
Social media; News coverage; Digital epidemiology; Infodemiology; Infoveillance; Infodemic; Digital epidemiology; Data science; Topic modeling; Pandemic; COVID-19; Reddit; Wikipedia; Information; Response; Risk perception; Behavior
JMIR Publications
Journal of Medical Internet Research. 2020;22(10):e21597
©Nicolò Gozzi, Michele Tizzani, Michele Starnini, Fabio Ciulla, Daniela Paolotti, André Panisson, Nicola Perra. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 12.10.2020. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
https://creativecommons.org/licenses/by/4.0/