Nipping in the Bud: Detection, Diffusion and Mitigation of Hate Speech on Social Media
It addresses the issue of hate speech for social media users and marginalized groups, but appears incremental as it builds on existing research without claiming major breakthroughs.
The paper tackles the problem of hate speech on social media by identifying methodological challenges and proposing solutions to limit its spread, though no concrete results or numbers are provided.
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.