论文标题
社交媒体上仇恨言论的病毒性
The Virality of Hate Speech on Social Media
论文作者
论文摘要
在线仇恨言论负责暴力攻击，例如匹兹堡犹太教堂在2018年拍摄，从而对弱势群体和整个社会构成了重大威胁。但是，关于使社交媒体上的仇恨言论传播开来的知之甚少。在本文中，我们收集n = 25,219的级联，并从X（以前称为Twitter）中收集65,946个转发，并将它们归类为可恶的与正常人。然后，我们使用广义的线性回归，基于作者和内容变量估算可恨与正常内容的传播的差异。因此，我们确定了重要的决定因素，以解释可恨与正常内容的传播差异。例如，由经过验证的用户撰写的可恨内容比未验证的内容的可恶内容更可能更容易流行：经过验证的用户的仇恨内容（与正常内容相反）的级联尺寸更大3.2倍，级联生寿命的3.2倍，而结构性病毒性则更大。总的来说，我们提供了有关社交媒体上仇恨言论的病毒性的新颖见解。
Online hate speech is responsible for violent attacks such as, e.g., the Pittsburgh synagogue shooting in 2018, thereby posing a significant threat to vulnerable groups and society in general. However, little is known about what makes hate speech on social media go viral. In this paper, we collect N = 25,219 cascades with 65,946 retweets from X (formerly known as Twitter) and classify them as hateful vs. normal. Using a generalized linear regression, we then estimate differences in the spread of hateful vs. normal content based on author and content variables. We thereby identify important determinants that explain differences in the spreading of hateful vs. normal content. For example, hateful content authored by verified users is disproportionally more likely to go viral than hateful content from non-verified ones: hateful content from a verified user (as opposed to normal content) has a 3.5 times larger cascade size, a 3.2 times longer cascade lifetime, and a 1.2 times larger structural virality. Altogether, we offer novel insights into the virality of hate speech on social media.
