美国国会大厦风暴期间的在线情绪:来自社交媒体网络Parler的证据

Johannes Jakubik, Michael Vössing, Nicolas Pröllochs, Dominik Bär, Stefan Feuerriegel
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引用次数: 3

摘要

2021年1月6日袭击美国国会大厦导致5人死亡,被广泛认为是对民主的攻击。这场风暴主要是通过Twitter和“Parler”等社交媒体网络协调的。然而,在袭击国会大厦期间,用户是如何在Parler上互动的,我们知之甚少。在这项工作中,我们研究了风暴期间Parler在时间和用户异质性方面的情绪动态。为此,我们将用户群划分为不同的组(例如,特朗普支持者和QAnon支持者)。我们使用情感计算来推断内容中的情感,从而使我们能够提供对在线情感的全面评估。我们的评估基于Parler的大型数据集,包括来自144,003个用户的717,300个帖子。我们发现,用户群对国会大厦的袭击反应总体上是负面的。与此类似,特朗普的支持者也表达了负面情绪和高度的不相信。与此相反,QAnon的支持者在风暴中并没有表现出更负面的情绪。我们进一步提供了跨平台分析,并比较了Parler和Twitter上的情感动态。我们的调查结果表明,与推特相比,Parler上对事件的负面反应相对较少,但反对和愤怒的程度更高。我们对研究的贡献有三个方面:(1)我们确定了风暴的在线情绪特征;(2)我们评估了Parler上不同用户群体的情感动态;(3)我们比较了Parler和Twitter上的情绪动态。因此,我们的工作为积极管理网络情绪以防止未来类似事件的发生提供了重要的启示。
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Online Emotions during the Storming of the U.S. Capitol: Evidence from the Social Media Network Parler
The storming of the U.S. Capitol on January 6, 2021 has led to the killing of 5 people and is widely regarded as an attack on democracy. The storming was largely coordinated through social media networks such as Twitter and "Parler". Yet little is known regarding how users interacted on Parler during the storming of the Capitol. In this work, we examine the emotion dynamics on Parler during the storming with regard to heterogeneity across time and users. For this, we segment the user base into different groups (e.g., Trump supporters and QAnon supporters). We use affective computing to infer the emotions in content, thereby allowing us to provide a comprehensive assessment of online emotions. Our evaluation is based on a large-scale dataset from Parler, comprising of 717,300 posts from 144,003 users. We find that the user base responded to the storming of the Capitol with an overall negative sentiment. Akin to this, Trump supporters also expressed a negative sentiment and high levels of unbelief. In contrast to that, QAnon supporters did not express a more negative sentiment during the storming. We further provide a cross-platform analysis and compare the emotion dynamics on Parler and Twitter. Our findings point at a comparatively less negative response to the incidents on Parler compared to Twitter accompanied by higher levels of disapproval and outrage. Our contribution to research is three-fold: (1) We identify online emotions that were characteristic of the storming; (2) we assess emotion dynamics across different user groups on Parler; (3) we compare the emotion dynamics on Parler and Twitter. Thereby, our work offers important implications for actively managing online emotions to prevent similar incidents in the future.
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