Vaccination trials on hold: malicious and low credibility content on Twitter during the AstraZeneca COVID-19 vaccine development.

IF 1.8 4区管理学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computational and Mathematical Organization Theory Pub Date : 2022-11-28 DOI:10.1007/s10588-022-09370-3

Sameera Horawalavithana, Ravindu De Silva, Nipuna Weerasekara, N G Kin Wai, Mohamed Nabeel, Buddhini Abayaratna, Charitha Elvitigala, Primal Wijesekera, Adriana Iamnitchi

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Abstract

The development of COVID-19 vaccines during the global pandemic that started in 2020 was marked by uncertainty and misinformation reflected also on social media. This paper provides a quantitative evaluation of the Uniform Resource Locators (URLs) shared on Twitter around the clinical trials of the AstraZeneca vaccine and their temporary interruption in September 2020. We analyzed URLs cited in Twitter messages before and after the temporary interruption of the vaccine development on September 9, 2020 to investigate the presence of low credibility and malicious information. We show that the halt of the AstraZeneca clinical trials prompted tweets that cast doubt, fear and vaccine opposition. We discovered a strong presence of URLs from low credibility or malicious websites, as classified by independent fact-checking organizations or identified by web hosting infrastructure features. Moreover, we identified what appears to be coordinated operations to artificially promote some of these URLs hosted on malicious websites.

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疫苗接种试验搁置：阿斯利康 COVID-19 疫苗研发期间 Twitter 上的恶意和低可信度内容。

在 2020 年开始的全球大流行期间，COVID-19 疫苗的开发充满了不确定性，社交媒体上也出现了错误信息。本文对围绕阿斯利康疫苗临床试验及其在 2020 年 9 月的临时中断在 Twitter 上共享的统一资源定位器（URL）进行了定量评估。我们分析了 2020 年 9 月 9 日疫苗研发临时中断前后 Twitter 消息中引用的 URL，以调查是否存在低可信度和恶意信息。我们发现，阿斯利康临床试验的中断引发了质疑、恐惧和反对疫苗的推文。我们发现了大量来自低可信度或恶意网站的 URL，这些 URL 由独立事实核查机构分类或由网络托管基础设施特征识别。此外，我们还发现了一些似乎是经过协调的操作，人为推广其中一些托管在恶意网站上的 URL。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computational and Mathematical Organization Theory COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

CiteScore

3.80

自引率

16.70%

发文量

审稿时长

>12 weeks

期刊介绍： Computational and Mathematical Organization Theory provides an international forum for interdisciplinary research that combines computation, organizations and society. The goal is to advance the state of science in formal reasoning, analysis, and system building drawing on and encouraging advances in areas at the confluence of social networks, artificial intelligence, complexity, machine learning, sociology, business, political science, economics, and operations research. The papers in this journal will lead to the development of newtheories that explain and predict the behaviour of complex adaptive systems, new computational models and technologies that are responsible to society, business, policy, and law, new methods for integrating data, computational models, analysis and visualization techniques. Various types of papers and underlying research are welcome. Papers presenting, validating, or applying models and/or computational techniques, new algorithms, dynamic metrics for networks and complex systems and papers comparing, contrasting and docking computational models are strongly encouraged. Both applied and theoretical work is strongly encouraged. The editors encourage theoretical research on fundamental principles of social behaviour such as coordination, cooperation, evolution, and destabilization. The editors encourage applied research representing actual organizational or policy problems that can be addressed using computational tools. Work related to fundamental concepts, corporate, military or intelligence issues are welcome.