Journal article
Public risk perception and emotion on Twitter during the Covid-19 pandemic
- Abstract:
- Successful navigation of the Covid-19 pandemic is predicated on public cooperation with safety measures and appropriate perception of risk, in which emotion and attention play important roles. Signatures of public emotion and attention are present in social media data, thus natural language analysis of this text enables near-to-real-time monitoring of indicators of public risk perception. We compare key epidemiological indicators of the progression of the pandemic with indicators of the public perception of the pandemic constructed from ∼20 million unique Covid-19-related tweets from 12 countries posted between 10th March and 14th June 2020. We find evidence of psychophysical numbing: Twitter users increasingly fixate on mortality, but in a decreasingly emotional and increasingly analytic tone. Semantic network analysis based on word co-occurrences reveals changes in the emotional framing of Covid-19 casualties that are consistent with this hypothesis. We also find that the average attention afforded to national Covid-19 mortality rates is modelled accurately with the Weber–Fechner and power law functions of sensory perception. Our parameter estimates for these models are consistent with estimates from psychological experiments, and indicate that users in this dataset exhibit differential sensitivity by country to the national Covid-19 death rates. Our work illustrates the potential utility of social media for monitoring public risk perception and guiding public communication during crisis scenarios.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, 5.7MB, Terms of use)
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- Publisher copy:
- 10.1007/s41109-020-00334-7
Authors
- Publisher:
- Springer
- Journal:
- Applied Network Science More from this journal
- Volume:
- 5
- Article number:
- 99
- Publication date:
- 2020-12-16
- Acceptance date:
- 2020-11-03
- DOI:
- EISSN:
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2364-8228
- Language:
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English
- Keywords:
- Pubs id:
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1151958
- Local pid:
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pubs:1151958
- Deposit date:
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2021-01-04
Terms of use
- Copyright holder:
- Joel Dyer and Blas Kolic
- Copyright date:
- 2020
- Rights statement:
- © The Author(s) 2020. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
- Licence:
- CC Attribution (CC BY)
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