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

Digital Health · 3 authors, 3 centres

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TL;DR

This study analyzed YouTube's recommendation algorithm for COVID-19 vaccine content in Arabic using network exposure models. It found that users had a low chance of exposure to anti-vaccine content in Arabic, with only the network starting from an anti-vaccine expert seed video showing a significant likelihood of such exposure.

Full summary

356 CHARS

The study created four networks of Arabic-language YouTube videos in April 2021 using pro-vaccine/anti-vaccine keyword searches and anti-vaccine seed videos to model user exposure. The network exposure model was used to analyze video content and network structures. Key results indicate that the majority of videos in the networks were not vaccine-related.

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other

SPECIALTY

public health

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AI pipeline

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100% · A

ORIGINAL PAPER

PMC10559698

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

Digital Health

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ABSTRACT

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. Keywords: YouTube, Arabic, vaccine hesitancy, vaccine acceptance, MENA, network exposure analysis

CITATION EXPORT

Nour Zeid, Lu Tang, Muhammad “Tuan” Amith. The spread of COVID-19 vaccine information in Arabic on YouTube: A network exposure study. Digital Health. (2023). https://doi.org/10.1177/20552076231205714

AUTHORS

3 · 3 CENTRES

Nour Zeid, Lu Tang, Muhammad “Tuan” Amith

1Department of Communication & Journalism, Texas A&M University, College Station, Texas, USA
2Biostatistics & Data Science, University of Texas Medical Branch, Denton, Texas, USA
3Department of Internal Medicine, University of Texas Medical Branch, Galveston, Texas, USA