Cargando…
Infodemic Signal Detection During the COVID-19 Pandemic: Development of a Methodology for Identifying Potential Information Voids in Online Conversations
BACKGROUND: The COVID-19 pandemic has been accompanied by an infodemic: excess information, including false or misleading information, in digital and physical environments during an acute public health event. This infodemic is leading to confusion and risk-taking behaviors that can be harmful to hea...
Autores principales: | , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8330887/ https://www.ncbi.nlm.nih.gov/pubmed/34447926 http://dx.doi.org/10.2196/30971 |
_version_ | 1783732814785544192 |
---|---|
author | Purnat, Tina D Vacca, Paolo Czerniak, Christine Ball, Sarah Burzo, Stefano Zecchin, Tim Wright, Amy Bezbaruah, Supriya Tanggol, Faizza Dubé, Ève Labbé, Fabienne Dionne, Maude Lamichhane, Jaya Mahajan, Avichal Briand, Sylvie Nguyen, Tim |
author_facet | Purnat, Tina D Vacca, Paolo Czerniak, Christine Ball, Sarah Burzo, Stefano Zecchin, Tim Wright, Amy Bezbaruah, Supriya Tanggol, Faizza Dubé, Ève Labbé, Fabienne Dionne, Maude Lamichhane, Jaya Mahajan, Avichal Briand, Sylvie Nguyen, Tim |
author_sort | Purnat, Tina D |
collection | PubMed |
description | BACKGROUND: The COVID-19 pandemic has been accompanied by an infodemic: excess information, including false or misleading information, in digital and physical environments during an acute public health event. This infodemic is leading to confusion and risk-taking behaviors that can be harmful to health, as well as to mistrust in health authorities and public health responses. The World Health Organization (WHO) is working to develop tools to provide an evidence-based response to the infodemic, enabling prioritization of health response activities. OBJECTIVE: In this work, we aimed to develop a practical, structured approach to identify narratives in public online conversations on social media platforms where concerns or confusion exist or where narratives are gaining traction, thus providing actionable data to help the WHO prioritize its response efforts to address the COVID-19 infodemic. METHODS: We developed a taxonomy to filter global public conversations in English and French related to COVID-19 on social media into 5 categories with 35 subcategories. The taxonomy and its implementation were validated for retrieval precision and recall, and they were reviewed and adapted as language about the pandemic in online conversations changed over time. The aggregated data for each subcategory were analyzed on a weekly basis by volume, velocity, and presence of questions to detect signals of information voids with potential for confusion or where mis- or disinformation may thrive. A human analyst reviewed and identified potential information voids and sources of confusion, and quantitative data were used to provide insights on emerging narratives, influencers, and public reactions to COVID-19–related topics. RESULTS: A COVID-19 public health social listening taxonomy was developed, validated, and applied to filter relevant content for more focused analysis. A weekly analysis of public online conversations since March 23, 2020, enabled quantification of shifting interests in public health–related topics concerning the pandemic, and the analysis demonstrated recurring voids of verified health information. This approach therefore focuses on the detection of infodemic signals to generate actionable insights to rapidly inform decision-making for a more targeted and adaptive response, including risk communication. CONCLUSIONS: This approach has been successfully applied to identify and analyze infodemic signals, particularly information voids, to inform the COVID-19 pandemic response. More broadly, the results have demonstrated the importance of ongoing monitoring and analysis of public online conversations, as information voids frequently recur and narratives shift over time. The approach is being piloted in individual countries and WHO regions to generate localized insights and actions; meanwhile, a pilot of an artificial intelligence–based social listening platform is using this taxonomy to aggregate and compare online conversations across 20 countries. Beyond the COVID-19 pandemic, the taxonomy and methodology may be adapted for fast deployment in future public health events, and they could form the basis of a routine social listening program for health preparedness and response planning. |
format | Online Article Text |
id | pubmed-8330887 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-83308872021-08-20 Infodemic Signal Detection During the COVID-19 Pandemic: Development of a Methodology for Identifying Potential Information Voids in Online Conversations Purnat, Tina D Vacca, Paolo Czerniak, Christine Ball, Sarah Burzo, Stefano Zecchin, Tim Wright, Amy Bezbaruah, Supriya Tanggol, Faizza Dubé, Ève Labbé, Fabienne Dionne, Maude Lamichhane, Jaya Mahajan, Avichal Briand, Sylvie Nguyen, Tim JMIR Infodemiology Original Paper BACKGROUND: The COVID-19 pandemic has been accompanied by an infodemic: excess information, including false or misleading information, in digital and physical environments during an acute public health event. This infodemic is leading to confusion and risk-taking behaviors that can be harmful to health, as well as to mistrust in health authorities and public health responses. The World Health Organization (WHO) is working to develop tools to provide an evidence-based response to the infodemic, enabling prioritization of health response activities. OBJECTIVE: In this work, we aimed to develop a practical, structured approach to identify narratives in public online conversations on social media platforms where concerns or confusion exist or where narratives are gaining traction, thus providing actionable data to help the WHO prioritize its response efforts to address the COVID-19 infodemic. METHODS: We developed a taxonomy to filter global public conversations in English and French related to COVID-19 on social media into 5 categories with 35 subcategories. The taxonomy and its implementation were validated for retrieval precision and recall, and they were reviewed and adapted as language about the pandemic in online conversations changed over time. The aggregated data for each subcategory were analyzed on a weekly basis by volume, velocity, and presence of questions to detect signals of information voids with potential for confusion or where mis- or disinformation may thrive. A human analyst reviewed and identified potential information voids and sources of confusion, and quantitative data were used to provide insights on emerging narratives, influencers, and public reactions to COVID-19–related topics. RESULTS: A COVID-19 public health social listening taxonomy was developed, validated, and applied to filter relevant content for more focused analysis. A weekly analysis of public online conversations since March 23, 2020, enabled quantification of shifting interests in public health–related topics concerning the pandemic, and the analysis demonstrated recurring voids of verified health information. This approach therefore focuses on the detection of infodemic signals to generate actionable insights to rapidly inform decision-making for a more targeted and adaptive response, including risk communication. CONCLUSIONS: This approach has been successfully applied to identify and analyze infodemic signals, particularly information voids, to inform the COVID-19 pandemic response. More broadly, the results have demonstrated the importance of ongoing monitoring and analysis of public online conversations, as information voids frequently recur and narratives shift over time. The approach is being piloted in individual countries and WHO regions to generate localized insights and actions; meanwhile, a pilot of an artificial intelligence–based social listening platform is using this taxonomy to aggregate and compare online conversations across 20 countries. Beyond the COVID-19 pandemic, the taxonomy and methodology may be adapted for fast deployment in future public health events, and they could form the basis of a routine social listening program for health preparedness and response planning. JMIR Publications 2021-07-28 /pmc/articles/PMC8330887/ /pubmed/34447926 http://dx.doi.org/10.2196/30971 Text en ©Tina D Purnat, Paolo Vacca, Christine Czerniak, Sarah Ball, Stefano Burzo, Tim Zecchin, Amy Wright, Supriya Bezbaruah, Faizza Tanggol, Ève Dubé, Fabienne Labbé, Maude Dionne, Jaya Lamichhane, Avichal Mahajan, Sylvie Briand, Tim Nguyen. Originally published in JMIR Infodemiology (https://infodemiology.jmir.org), 28.07.2021. https://creativecommons.org/licenses/by/4.0/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 JMIR Infodemiology, is properly cited. The complete bibliographic information, a link to the original publication on https://infodemiology.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Purnat, Tina D Vacca, Paolo Czerniak, Christine Ball, Sarah Burzo, Stefano Zecchin, Tim Wright, Amy Bezbaruah, Supriya Tanggol, Faizza Dubé, Ève Labbé, Fabienne Dionne, Maude Lamichhane, Jaya Mahajan, Avichal Briand, Sylvie Nguyen, Tim Infodemic Signal Detection During the COVID-19 Pandemic: Development of a Methodology for Identifying Potential Information Voids in Online Conversations |
title | Infodemic Signal Detection During the COVID-19 Pandemic: Development of a Methodology for Identifying Potential Information Voids in Online Conversations |
title_full | Infodemic Signal Detection During the COVID-19 Pandemic: Development of a Methodology for Identifying Potential Information Voids in Online Conversations |
title_fullStr | Infodemic Signal Detection During the COVID-19 Pandemic: Development of a Methodology for Identifying Potential Information Voids in Online Conversations |
title_full_unstemmed | Infodemic Signal Detection During the COVID-19 Pandemic: Development of a Methodology for Identifying Potential Information Voids in Online Conversations |
title_short | Infodemic Signal Detection During the COVID-19 Pandemic: Development of a Methodology for Identifying Potential Information Voids in Online Conversations |
title_sort | infodemic signal detection during the covid-19 pandemic: development of a methodology for identifying potential information voids in online conversations |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8330887/ https://www.ncbi.nlm.nih.gov/pubmed/34447926 http://dx.doi.org/10.2196/30971 |
work_keys_str_mv | AT purnattinad infodemicsignaldetectionduringthecovid19pandemicdevelopmentofamethodologyforidentifyingpotentialinformationvoidsinonlineconversations AT vaccapaolo infodemicsignaldetectionduringthecovid19pandemicdevelopmentofamethodologyforidentifyingpotentialinformationvoidsinonlineconversations AT czerniakchristine infodemicsignaldetectionduringthecovid19pandemicdevelopmentofamethodologyforidentifyingpotentialinformationvoidsinonlineconversations AT ballsarah infodemicsignaldetectionduringthecovid19pandemicdevelopmentofamethodologyforidentifyingpotentialinformationvoidsinonlineconversations AT burzostefano infodemicsignaldetectionduringthecovid19pandemicdevelopmentofamethodologyforidentifyingpotentialinformationvoidsinonlineconversations AT zecchintim infodemicsignaldetectionduringthecovid19pandemicdevelopmentofamethodologyforidentifyingpotentialinformationvoidsinonlineconversations AT wrightamy infodemicsignaldetectionduringthecovid19pandemicdevelopmentofamethodologyforidentifyingpotentialinformationvoidsinonlineconversations AT bezbaruahsupriya infodemicsignaldetectionduringthecovid19pandemicdevelopmentofamethodologyforidentifyingpotentialinformationvoidsinonlineconversations AT tanggolfaizza infodemicsignaldetectionduringthecovid19pandemicdevelopmentofamethodologyforidentifyingpotentialinformationvoidsinonlineconversations AT dubeeve infodemicsignaldetectionduringthecovid19pandemicdevelopmentofamethodologyforidentifyingpotentialinformationvoidsinonlineconversations AT labbefabienne infodemicsignaldetectionduringthecovid19pandemicdevelopmentofamethodologyforidentifyingpotentialinformationvoidsinonlineconversations AT dionnemaude infodemicsignaldetectionduringthecovid19pandemicdevelopmentofamethodologyforidentifyingpotentialinformationvoidsinonlineconversations AT lamichhanejaya infodemicsignaldetectionduringthecovid19pandemicdevelopmentofamethodologyforidentifyingpotentialinformationvoidsinonlineconversations AT mahajanavichal infodemicsignaldetectionduringthecovid19pandemicdevelopmentofamethodologyforidentifyingpotentialinformationvoidsinonlineconversations AT briandsylvie infodemicsignaldetectionduringthecovid19pandemicdevelopmentofamethodologyforidentifyingpotentialinformationvoidsinonlineconversations AT nguyentim infodemicsignaldetectionduringthecovid19pandemicdevelopmentofamethodologyforidentifyingpotentialinformationvoidsinonlineconversations |