JOUR AU - Al-Rawi, Ahmed AU - Fakida, Abdelrahman AU - Grounds, Kelly PY - 2022 DA - 2022/7/26 TI -调查2019冠状病毒病在推特上的阿拉伯语错误信息:内容分析乔- JMIR Infodemiology SP - e37007六世- 2 - 2 KW - COVID-19 KW -阿拉伯世界KW - Twitter KW -错误KW -疫苗接种KW - Infodemiology KW -疫苗犹豫KW - infoveillance KW -健康信息KW -社会媒体KW——社会媒体内容KW -内容分析KW - Twitter分析AB -背景:COVID-19大流行已经发生并发的infodemic错误信息的病毒。主要在社交媒体上传播，已经有一个重要的学术努力来理解这个信息大流行的英语方面。但是，对阿拉伯方面的注意要少得多。目的:迫切需要对阿拉伯国家COVID-19虚假信息的规模进行调查。这项研究实证研究了阿拉伯语使用者如何在Twitter上使用特定的标签来表达反疫苗和反流行病的观点，以揭示他们使用社交媒体的趋势。通过探讨这一主题，我们的目标是填补有助于理解围绕COVID-19的阿拉伯语阴谋的文献空白。方法:本研究采用内容分析来了解13个流行的阿拉伯语标签在抗疫苗社区中的使用情况。我们使用Twitter学术API v2来搜索从2006年8月1日开始到2021年10月10日的标签。在从Twitter下载了一个大型数据集之后，我们使用紧急编码确定了样本数据集中的主要类别或主题。 Emergent coding was chosen because of its ability to inductively identify the themes that repeatedly emerged from the data set. Then, after revising the coding scheme, we coded the rest of the tweets and examined the results. In the second attempt and with a modified codebook, an acceptable intercoder agreement was reached (Krippendorff α≥.774). Results: In total, we found 476,048 tweets, mostly posted in 2021. First, the topic of infringing on civil liberties (n=483, 41.1%) covers ways that governments have allegedly infringed on civil liberties during the pandemic and unfair restrictions that have been imposed on unvaccinated individuals. Users here focus on topics concerning their civil liberties and freedoms, claiming that governments violated such rights following the pandemic. Notably, users denounce government efforts to force them to take any of the COVID-19 vaccines for different reasons. This was followed by vaccine-related conspiracies (n=476, 40.5%), including a Deep State dictating pandemic policies, mistrusting vaccine efficacy, and discussing unproven treatments. Although users tweeted about a range of different conspiracy theories, mistrusting the vaccine’s efficacy, false or exaggerated claims about vaccine risks and vaccine-related diseases, and governments and pharmaceutical companies profiting from vaccines and intentionally risking the general public health appeared the most. Finally, calls for action (n=149, 12.6%) encourage individuals to participate in civil demonstrations. These calls range from protesting to encouraging other users to take action about the vaccine mandate. For each of these categories, we also attempted to trace the logic behind the different categories by exploring different types of conspiracy theories for each category. Conclusions: Based on our findings, we were able to identify 3 prominent topics that were prevalent amongst Arabic speakers on Twitter. These categories focused on violations of civil liberties by governments, conspiracy theories about the vaccines, and calls for action. Our findings also highlight the need for more research to better understand the impact of COVID-19 disinformation on the Arab world. SN - 2564-1891 UR - https://infodemiology.www.mybigtv.com/2022/2/e37007 UR - https://doi.org/10.2196/37007 UR - http://www.ncbi.nlm.nih.gov/pubmed/35915823 DO - 10.2196/37007 ID - info:doi/10.2196/37007 ER -