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The spread of COVID-19 vaccine information in Arabic on YouTube: A network exposure study

OBJECTIVE: The Arabic-speaking world had the lowest vaccine rates worldwide. The region's increasing reliance on social media as a source of COVID-19 information coupled with the increasing popularity of YouTube in the Middle East and North Africa region begs the question of what COVID-19 vacci...

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Autores principales: Zeid, Nour, Tang, Lu, Amith, Muhammad “Tuan”
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10559698/
https://www.ncbi.nlm.nih.gov/pubmed/37808239
http://dx.doi.org/10.1177/20552076231205714
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author Zeid, Nour
Tang, Lu
Amith, Muhammad “Tuan”
author_facet Zeid, Nour
Tang, Lu
Amith, Muhammad “Tuan”
author_sort Zeid, Nour
collection PubMed
description OBJECTIVE: The Arabic-speaking world had the lowest vaccine rates worldwide. The region's increasing reliance on social media as a source of COVID-19 information coupled with the increasing popularity of YouTube in the Middle East and North Africa region begs the question of what COVID-19 vaccine content is available in Arabic on YouTube. Given the platform's reputation for being a hotbed for vaccine-related misinformation in English, this study explored the COVID-19 vaccine-related content an individual is likely to be exposed to on YouTube when using keyword-based search or redirected to YouTube from another platform from an anti-vaccine seed video in Arabic. METHODS: Only using the Arabic language, four networks of videos based on YouTube's recommendations were created in April 2021. Two search networks were created based on Arabic pro-vaccine and anti-vaccine keywords, and two seed networks were created from conspiracy theory and anti-vaccine expert seed videos. The network exposure model was used to examine the video contents and network structures. RESULTS: Results show that users had a low chance of being exposed to anti-vaccine content in Arabic compared to the results of a previous study of YouTube content in English. Of the four networks, only the anti-vaccine expert network had a significant likelihood of exposing the user to more anti-vaccine videos. Implications were discussed. CONCLUSION: YouTube deserves credit for its efforts to clean up and limit anti-vaccine content exposure in Arabic on its platform, but continuous evaluations of the algorithm functionality are warranted.
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spelling pubmed-105596982023-10-08 The spread of COVID-19 vaccine information in Arabic on YouTube: A network exposure study Zeid, Nour Tang, Lu Amith, Muhammad “Tuan” Digit Health Original Research OBJECTIVE: The Arabic-speaking world had the lowest vaccine rates worldwide. The region's increasing reliance on social media as a source of COVID-19 information coupled with the increasing popularity of YouTube in the Middle East and North Africa region begs the question of what COVID-19 vaccine content is available in Arabic on YouTube. Given the platform's reputation for being a hotbed for vaccine-related misinformation in English, this study explored the COVID-19 vaccine-related content an individual is likely to be exposed to on YouTube when using keyword-based search or redirected to YouTube from another platform from an anti-vaccine seed video in Arabic. METHODS: Only using the Arabic language, four networks of videos based on YouTube's recommendations were created in April 2021. Two search networks were created based on Arabic pro-vaccine and anti-vaccine keywords, and two seed networks were created from conspiracy theory and anti-vaccine expert seed videos. The network exposure model was used to examine the video contents and network structures. RESULTS: Results show that users had a low chance of being exposed to anti-vaccine content in Arabic compared to the results of a previous study of YouTube content in English. Of the four networks, only the anti-vaccine expert network had a significant likelihood of exposing the user to more anti-vaccine videos. Implications were discussed. CONCLUSION: YouTube deserves credit for its efforts to clean up and limit anti-vaccine content exposure in Arabic on its platform, but continuous evaluations of the algorithm functionality are warranted. SAGE Publications 2023-10-06 /pmc/articles/PMC10559698/ /pubmed/37808239 http://dx.doi.org/10.1177/20552076231205714 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Original Research
Zeid, Nour
Tang, Lu
Amith, Muhammad “Tuan”
The spread of COVID-19 vaccine information in Arabic on YouTube: A network exposure study
title The spread of COVID-19 vaccine information in Arabic on YouTube: A network exposure study
title_full The spread of COVID-19 vaccine information in Arabic on YouTube: A network exposure study
title_fullStr The spread of COVID-19 vaccine information in Arabic on YouTube: A network exposure study
title_full_unstemmed The spread of COVID-19 vaccine information in Arabic on YouTube: A network exposure study
title_short The spread of COVID-19 vaccine information in Arabic on YouTube: A network exposure study
title_sort spread of covid-19 vaccine information in arabic on youtube: a network exposure study
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10559698/
https://www.ncbi.nlm.nih.gov/pubmed/37808239
http://dx.doi.org/10.1177/20552076231205714
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