ArticleDigital health
The spread of COVID-19 vaccine information in Arabic on YouTube: A network exposure study.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
3 authors.
Funding
Abstract
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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