论文标题
在社交媒体上咬住芽：仇恨言论的检测，扩散和缓解仇恨言论
Nipping in the Bud: Detection, Diffusion and Mitigation of Hate Speech on Social Media
论文作者
论文摘要
自从社交媒体使用的扩散以来，仇恨言论已成为一场重大危机。仇恨的内容可以迅速传播，并创造一个困扰和敌意的环境。此外，可以认为可恨的是上下文，随着时间而变化。尽管在线仇恨言论降低了已经边缘化团体自由参与讨论的能力，但离线仇恨言论会导致仇恨犯罪和对个人和社区的暴力行为。仇恨言论的多方面性质及其现实世界的影响已经引起了数据挖掘和机器学习社区的兴趣。尽管我们尽了最大的努力，但对于研究人员和从业者来说，仇恨言论仍然是一个回避的问题。本文提出了阻碍自动仇恨缓解系统的方法论挑战。这些挑战激发了我们在更广泛的领域打击网络上可恨内容的领域。我们讨论了一系列建议的解决方案，以限制仇恨言论在社交媒体上的传播。
Since the proliferation of social media usage, hate speech has become a major crisis. Hateful content can spread quickly and create an environment of distress and hostility. Further, what can be considered hateful is contextual and varies with time. While online hate speech reduces the ability of already marginalised groups to participate in discussion freely, offline hate speech leads to hate crimes and violence against individuals and communities. The multifaceted nature of hate speech and its real-world impact have already piqued the interest of the data mining and machine learning communities. Despite our best efforts, hate speech remains an evasive issue for researchers and practitioners alike. This article presents methodological challenges that hinder building automated hate mitigation systems. These challenges inspired our work in the broader area of combating hateful content on the web. We discuss a series of our proposed solutions to limit the spread of hate speech on social media.
