Natl Sci Open
Volume 2, Number 6, 2023
|Number of page(s)
|31 March 2023
Information overload: How hot topics distract from news—COVID-19 spread in the US
1 Computational Communication Collaboratory, Nanjing University, Nanjing 210093, China
2 Complex Systems Group, Department of Mathematics and Statistics, The University of Western Australia, Perth 6009, Australia
3 School of Media and Communication, Shenzhen University, Shenzhen 518060, China
Revised: 29 November 2022
Accepted: 5 December 2022
Information dissemination and the associated change of individual behavior can significantly slow the spread of an epidemic. However, major social events which attract public attention will disturb information spread and affect epidemic transmission in ways that have not been readily quantified. We investigate the interplay between disease spreading and disease-related information dissemination in a two-layer network. We employ the SIR-UAU model with a time dependent coefficient to denote information dissemination. We found that major social events are equivalent to perturbations of information dissemination in certain time intervals and will consequently weaken the effect of information dissemination, and increase prevalence of infection. Our simulation results agree well with the trends observed from real-world data sets. We found that two specific major events explain the trend of the coronavirus epidemic in the US: the online propaganda and international agenda setting of Donald Trump early in 2020 and the 2020 US Presidential Election.
Key words: information spreading / COVID-19 / SIR model / 2020 US Presidential Election / Altmetric / network propagation
© The Author(s) 2023. Published by Science Press and EDP Sciences
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