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Qualitative analysis of visual risk communication on twitter during the Covid-19 pandemic

BACKGROUND: The Covid-19 pandemic is characterized by uncertainty and constant change, forcing governments and health authorities to ramp up risk communication efforts. Consequently, visuality and social media platforms like Twitter have come to play a vital role in disseminating prevention messages...

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Autores principales: Sleigh, Joanna, Amann, Julia, Schneider, Manuel, Vayena, Effy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8079223/
https://www.ncbi.nlm.nih.gov/pubmed/33906626
http://dx.doi.org/10.1186/s12889-021-10851-4
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author Sleigh, Joanna
Amann, Julia
Schneider, Manuel
Vayena, Effy
author_facet Sleigh, Joanna
Amann, Julia
Schneider, Manuel
Vayena, Effy
author_sort Sleigh, Joanna
collection PubMed
description BACKGROUND: The Covid-19 pandemic is characterized by uncertainty and constant change, forcing governments and health authorities to ramp up risk communication efforts. Consequently, visuality and social media platforms like Twitter have come to play a vital role in disseminating prevention messages widely. Yet to date, only little is known about what characterizes visual risk communication during the Covid-19 pandemic. To address this gap in the literature, this study’s objective was to determine how visual risk communication was used on Twitter to promote the World Health Organisations (WHO) recommended preventative behaviours and how this communication changed over time. METHODS: We sourced Twitter’s 500 most retweeted Covid-19 messages for each month from January–October 2020 using Crowdbreaks. For inclusion, tweets had to have visuals, be in English, come from verified accounts, and contain one of the keywords ‘covid19’, ‘coronavirus’, ‘corona’, or ‘covid’. Following a retrospective approach, we then performed a qualitative content analysis of the 616 tweets meeting inclusion criteria. RESULTS: Our results show communication dynamics changed over the course of the pandemic. At the start, most retweeted preventative messages came from the media and health and government institutions, but overall, personal accounts with many followers (51.3%) predominated, and their tweets had the highest spread (10.0%, i.e., retweet count divided by followers). Messages used mostly photographs and images were found to be rich with information. 78.1% of Tweets contained 1–2 preventative messages, whereby ‘stay home’ and ‘wear a mask’ frequented most. Although more tweets used health loss framing, health gain messages spread more. CONCLUSION: Our findings can inform the didactics of future crisis communication. The results underscore the value of engaging individuals, particularly influencers, as advocates to spread health risk messages and promote solidarity. Further, our findings on the visual characteristic of the most retweeted tweets highlight factors that health and government organisations should consider when creating visual health messages for Twitter. However, that more tweets used the emotive medium of photographs often combined with health loss framing raises concerns about persuasive tactics. More research is needed to understand the implications of framing and its impact on public perceptions and behaviours. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-021-10851-4.
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spelling pubmed-80792232021-04-28 Qualitative analysis of visual risk communication on twitter during the Covid-19 pandemic Sleigh, Joanna Amann, Julia Schneider, Manuel Vayena, Effy BMC Public Health Research BACKGROUND: The Covid-19 pandemic is characterized by uncertainty and constant change, forcing governments and health authorities to ramp up risk communication efforts. Consequently, visuality and social media platforms like Twitter have come to play a vital role in disseminating prevention messages widely. Yet to date, only little is known about what characterizes visual risk communication during the Covid-19 pandemic. To address this gap in the literature, this study’s objective was to determine how visual risk communication was used on Twitter to promote the World Health Organisations (WHO) recommended preventative behaviours and how this communication changed over time. METHODS: We sourced Twitter’s 500 most retweeted Covid-19 messages for each month from January–October 2020 using Crowdbreaks. For inclusion, tweets had to have visuals, be in English, come from verified accounts, and contain one of the keywords ‘covid19’, ‘coronavirus’, ‘corona’, or ‘covid’. Following a retrospective approach, we then performed a qualitative content analysis of the 616 tweets meeting inclusion criteria. RESULTS: Our results show communication dynamics changed over the course of the pandemic. At the start, most retweeted preventative messages came from the media and health and government institutions, but overall, personal accounts with many followers (51.3%) predominated, and their tweets had the highest spread (10.0%, i.e., retweet count divided by followers). Messages used mostly photographs and images were found to be rich with information. 78.1% of Tweets contained 1–2 preventative messages, whereby ‘stay home’ and ‘wear a mask’ frequented most. Although more tweets used health loss framing, health gain messages spread more. CONCLUSION: Our findings can inform the didactics of future crisis communication. The results underscore the value of engaging individuals, particularly influencers, as advocates to spread health risk messages and promote solidarity. Further, our findings on the visual characteristic of the most retweeted tweets highlight factors that health and government organisations should consider when creating visual health messages for Twitter. However, that more tweets used the emotive medium of photographs often combined with health loss framing raises concerns about persuasive tactics. More research is needed to understand the implications of framing and its impact on public perceptions and behaviours. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-021-10851-4. BioMed Central 2021-04-28 /pmc/articles/PMC8079223/ /pubmed/33906626 http://dx.doi.org/10.1186/s12889-021-10851-4 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Sleigh, Joanna
Amann, Julia
Schneider, Manuel
Vayena, Effy
Qualitative analysis of visual risk communication on twitter during the Covid-19 pandemic
title Qualitative analysis of visual risk communication on twitter during the Covid-19 pandemic
title_full Qualitative analysis of visual risk communication on twitter during the Covid-19 pandemic
title_fullStr Qualitative analysis of visual risk communication on twitter during the Covid-19 pandemic
title_full_unstemmed Qualitative analysis of visual risk communication on twitter during the Covid-19 pandemic
title_short Qualitative analysis of visual risk communication on twitter during the Covid-19 pandemic
title_sort qualitative analysis of visual risk communication on twitter during the covid-19 pandemic
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8079223/
https://www.ncbi.nlm.nih.gov/pubmed/33906626
http://dx.doi.org/10.1186/s12889-021-10851-4
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