论文标题
在美国国会大厦暴风雨期间的在线情绪:社交媒体网络Parler的证据
Online Emotions During the Storming of the U.S. Capitol: Evidence from the Social Media Network Parler
论文作者
论文摘要
2021年1月6日,美国国会大厦的袭击导致杀害5人,被广泛认为是对民主的攻击。暴风雨在很大程度上通过帕勒(Parler)等社交媒体网络进行了协调。然而,关于在国会大厦猛烈袭击期间,用户如何在Parler上进行互动知之甚少。在这项工作中,我们研究了parler在暴风雨期间关于跨时间和用户异质性的情感动态。为此,我们将用户群分为不同的组(例如,特朗普支持者和Qanon支持者)。我们使用情感计算(Kratzwald等,2018)来推断内容中的情绪,从而使我们能够对在线情绪进行全面评估。我们的评估基于Parler的大规模数据集,其中包括来自144,003位用户的717,300个帖子。我们发现,用户群对国会大厦的袭击做出了总体负面情绪的反应。与此类似,特朗普的支持者也表达了负面情绪和高水平的不信。与此相反,Qanon的支持者在暴风雨期间没有表达出更负面的情绪。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。我们的发现指出,与Twitter相比,对Parler事件的负面反应相对较少,而不赞成和愤怒程度更高。我们对研究的贡献是三个方面的:(1)我们确定了在线情绪的特征,这些情绪是暴风雨的特征; (2)我们评估Parler上不同用户群体的情绪动态; (3)我们比较了Parler和Twitter上的情感动态。因此,我们的工作为积极管理在线情绪以防止将来的事件具有重要意义。
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 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 (Kratzwald et al. 2018) to infer the emotions in the contents, 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.