Zeid Nour, Tang Lu, Amith Muhammad Tuan
Department of Communication & Journalism, Texas A&M University, College Station, Texas, USA.
Biostatistics & Data Science, University of Texas Medical Branch, Denton, Texas, USA.
Digit Health. 2023 Oct 6;9:20552076231205714. doi: 10.1177/20552076231205714. eCollection 2023 Jan-Dec.
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.
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 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.
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.
阿拉伯语地区的疫苗接种率在全球处于最低水平。该地区越来越依赖社交媒体作为新冠病毒信息的来源，再加上YouTube在中东和北非地区日益普及，这引发了一个问题：YouTube上有哪些阿拉伯语的新冠疫苗相关内容。鉴于该平台在英语领域因充斥疫苗相关错误信息而声名狼藉，本研究探讨了个人在使用基于关键词的搜索或从阿拉伯语反疫苗种子视频从另一个平台重定向到YouTube时，可能接触到的与新冠疫苗相关的内容。
仅使用阿拉伯语，于2021年4月根据YouTube的推荐创建了四个视频网络。基于支持疫苗接种和反对疫苗接种的阿拉伯语关键词创建了两个搜索网络，并从阴谋论和反疫苗专家种子视频创建了两个种子网络。使用网络曝光模型来检查视频内容和网络结构。
结果表明，与之前一项关于英语YouTube内容的研究结果相比，用户接触阿拉伯语反疫苗内容的机会较低。在这四个网络中，只有反疫苗专家网络有显著可能性让用户接触到更多反疫苗视频。对相关影响进行了讨论。
YouTube在清理和限制其平台上阿拉伯语反疫苗内容曝光方面所做的努力值得称赞，但仍需对算法功能进行持续评估。
