推特上阿拉伯语新冠肺炎虚假信息调查:内容分析

IF 3.5 Q1 HEALTH CARE SCIENCES & SERVICES
JMIR infodemiology Pub Date : 2022-07-26 eCollection Date: 2022-07-01 DOI:10.2196/37007
Ahmed Al-Rawi, Abdelrahman Fakida, Kelly Grounds
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引用次数: 7

摘要

背景:2019冠状病毒病大流行与有关该病毒的错误信息大流行同时发生。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。之所以选择紧急编码,是因为它能够归纳地识别数据集中反复出现的主题。然后,在修改编码方案后,我们对其余的tweet进行编码并检查结果。在第二次尝试中,使用修改后的码本,达成了可接受的编码间协议(Krippendorff α≥.774)。结果:我们总共发现了476048条推文,其中大部分是在2021年发布的。首先,侵犯公民自由的主题(n=483, 41.1%)涵盖了据称政府在大流行期间侵犯公民自由的方式,以及对未接种疫苗的个人施加的不公平限制。这里的用户关注与他们的公民自由和自由有关的话题,声称政府在大流行之后侵犯了这些权利。值得注意的是,用户谴责政府出于不同原因强迫他们接种任何新冠病毒疫苗的努力。紧随其后的是与疫苗有关的阴谋(n=476, 40.5%),包括深层政府支配流行病政策、不信任疫苗效力以及讨论未经证实的治疗方法。尽管用户在推特上发布了一系列不同的阴谋论,但最常见的是不信任疫苗的功效,对疫苗风险和疫苗相关疾病的虚假或夸大声明,以及政府和制药公司从疫苗中获利并故意冒公众健康风险。最后,行动呼吁(n=149, 12.6%)鼓励个人参与民间示威。这些呼吁的范围从抗议到鼓励其他用户对疫苗授权采取行动。对于每一个类别,我们也试图通过探索每个类别的不同类型的阴谋论来追踪不同类别背后的逻辑。结论:基于我们的发现,我们能够确定Twitter上阿拉伯语使用者中流行的3个突出话题。这些分类集中在政府对公民自由的侵犯,关于疫苗的阴谋论,以及呼吁采取行动。我们的研究结果还强调,需要进行更多研究,以更好地了解COVID-19虚假信息对阿拉伯世界的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Investigation of COVID-19 Misinformation in Arabic on Twitter: Content Analysis.

Investigation of COVID-19 Misinformation in Arabic on Twitter: Content Analysis.

Background: The COVID-19 pandemic has been occurring concurrently with an infodemic of misinformation about the virus. Spreading primarily on social media, there has been a significant academic effort to understand the English side of this infodemic. However, much less attention has been paid to the Arabic side.

Objective: There is an urgent need to examine the scale of Arabic COVID-19 disinformation. This study empirically examines how Arabic speakers use specific hashtags on Twitter to express antivaccine and antipandemic views to uncover trends in their social media usage. By exploring this topic, we aim to fill a gap in the literature that can help understand conspiracies in Arabic around COVID-19.

Methods: This study used content analysis to understand how 13 popular Arabic hashtags were used in antivaccine communities. We used Twitter Academic API v2 to search for the hashtags from the beginning of August 1, 2006, until October 10, 2021. After downloading a large data set from Twitter, we identified major categories or topics in the sample data set using emergent coding. 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.

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