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Hunting Conspiracy Theories During the COVID-19 Pandemic
Social Media + Society ( IF 5.5 ) Pub Date : 2021-09-06 , DOI: 10.1177/20563051211043212
J. D. Moffitt 1 , Catherine King 1 , Kathleen M. Carley 1
Affiliation  

The fear of the unknown combined with the isolation generated by COVID-19 has created a fertile environment for strong disinformation, otherwise known as conspiracy theories, to flourish. Because conspiracy theories often contain a kernel of truth and feature a strong adversarial “other,” they serve as the perfect vehicle for maligned actors to use in influence campaigns. To explore the importance of conspiracies in the spread of dis-/mis-information, we propose the usage of state-of-the-art, tuned language models to classify tweets as conspiratorial or not. This model is based on the Bidirectional Encoder Representations from Transformers (BERT) model developed by Google researchers. The classification method expedites analysis by automating a process that is currently done manually (identifying tweets that promote conspiracy theories). We identified COVID-19 origin conspiracy theory tweets using this method and then used social cybersecurity methods to analyze communities, spreaders, and characteristics of the different origin-related conspiracy theory narratives. We found that tweets about conspiracy theories were supported by news sites with low fact-checking scores and amplified by bots who were more likely to link to prominent Twitter users than in non-conspiracy tweets. We also found different patterns in conspiracy vs. non-conspiracy conversations in terms of hashtag usage, identity, and country of origin. This analysis shows how we can better understand who spreads conspiracy theories and how they are spreading them.



中文翻译:

在 COVID-19 大流行期间寻找阴谋论

对未知的恐惧加上 COVID-19 产生的孤立,为强烈的虚假信息(也称为阴谋论)的蓬勃发展创造了肥沃的环境。由于阴谋论通常包含真相的核心,并具有强大的对抗性“他者”,因此它们是被诽谤者在影响力活动中使用的完美工具。为了探索阴谋在虚假/错误信息传播中的重要性,我们建议使用最先进的、经过调整的语言模型来将推文分类为阴谋与否。该模型基于 Google 研究人员开发的 Bidirectional Encoder Representations from Transformers (BERT) 模型。分类方法通过自动化当前手动完成的过程(识别宣传阴谋论的推文)来加速分析。我们使用这种方法识别了 COVID-19 起源阴谋论推文,然后使用社会网络安全方法来分析不同起源相关阴谋论叙述的社区、传播者和特征。我们发现,有关阴谋论的推文得到了事实核查分数较低的新闻网站的支持,并且被比非阴谋推文更有可能链接到著名 Twitter 用户的机器人放大了。我们还发现,在主题标签使用、身份和原籍国方面,阴谋与非阴谋对话的模式不同。该分析显示了我们如何更好地了解谁在传播阴谋论以及他们如何传播阴谋论。和不同起源相关的阴谋论叙事的特点。我们发现,有关阴谋论的推文得到了事实核查分数较低的新闻网站的支持,并且被比非阴谋推文更有可能链接到著名 Twitter 用户的机器人放大了。我们还发现,在主题标签使用、身份和原籍国方面,阴谋与非阴谋对话的模式不同。该分析显示了我们如何更好地了解谁在传播阴谋论以及他们如何传播阴谋论。和不同起源相关的阴谋论叙事的特点。我们发现,有关阴谋论的推文得到了事实核查分数较低的新闻网站的支持,并且被比非阴谋推文更有可能链接到著名 Twitter 用户的机器人放大了。我们还发现,在主题标签使用、身份和原籍国方面,阴谋与非阴谋对话的模式不同。该分析显示了我们如何更好地了解谁在传播阴谋论以及他们如何传播阴谋论。我们还发现,在主题标签使用、身份和原籍国方面,阴谋与非阴谋对话的模式不同。该分析显示了我们如何更好地了解谁在传播阴谋论以及他们如何传播阴谋论。我们还发现,在主题标签使用、身份和原籍国方面,阴谋与非阴谋对话的模式不同。该分析显示了我们如何更好地了解谁在传播阴谋论以及他们如何传播阴谋论。

更新日期:2021-09-06
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