Assessing the risks of ‘infodemics’ in response to COVID-19 epidemics

By Riccardo Gallotti, F. Valle, Nicola Castaldo, P. Sacco, Manlio De Domenico. Nature Human Behaviour, 2020

548 citations recorded by the source corpus.

During COVID-19, governments and the public are fighting not only a pandemic but also a co-evolving infodemic—the rapid and far-reaching spread of information of questionable quality. We analysed more than 100 million Twitter messages posted worldwide during the early stages of epidemic spread across countries (from 22 January to 10 March 2020) and classified the reliability of the news being circulated. We developed an Infodemic Risk Index to capture the magnitude of exposure to unreliable news across countries. We found that measurable waves of potentially unreliable information preceded the rise of COVID-19 infections, exposing entire countries to falsehoods that pose a serious threat to public health. As infections started to rise, reliable information quickly became more dominant, and Twitter content shifted towards more credible informational sources. Infodemic early-warning signals provide important cues for misinformation mitigation by means of adequate communication strategies. An analysis of news shared on Twitter estimates the level of infodemic risk associated with COVID-19 across countries. Epidemic spread and infodemic risk co-evolve, with reliable information becoming more dominant as infection rates rise locally.

Subfields: Health, Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/e5f2e58c649ce8ff1e1b358a1d96fe17dac40ca3

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Nicola Perra

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Citations in the corpus

Robustness and resilience of complex networks (2024, background): “…resilience at a societal level: it is well known that information spreading can represent a serious threat to public health systems, polarize debates and undermine public trust in democratic institutions 167,168 , but spreading can also have positive impacts such as diffusing innovations 166,169 .”
Adoption of Digital Technologies in Health Care During the COVID-19 Pandemic: Systematic Review of Early Scientific Literature (2020, background): “[121] 1 N/A 0 [53] 1 0 [1] 1 [18,42,127] 3 [1,18,21,31,42, 56,74,127] 8 [1] 1 Internet search engines, social media”
An exploratory study of COVID-19 misinformation on Twitter (2020, unknown): “Although there have already been some indications that bots might be involved [17,19,30], the majority of posts is generated by accounts that are likely to be human [19].”
Growing polarization around climate change on social media (2021, background): “Historically, the dominant focus of these studies has been political polarisation in the United States and Europe [20, 23], with a recent shift towards studying the Covid-19 discussion [17, 21, 22].”
Mapping the Landscape of Artificial Intelligence Applications against COVID-19 (2020, background): “[123] † analyze social media posts on Twitter across 64 languages.”
Non-pharmaceutical interventions during the COVID-19 pandemic: A review (2020, background): “[374] studies he communication strategies of official accounts (i.e., public health, emergency services, officials) on Twitter during he evolution of the emergency.”
A decade of social bot detection (2020, result): “Similar results were obtained about the presence of bots in online cryptocurrency discussions [24] and as part of the “infodemics” about the COVID-19 pandemic [14].”