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Since the proliferation of social media usage, hate speech has become a majorcrisis. Hateful content can spread quickly and create an environment ofdistress and hostility. Further, what can be considered hateful is contextualand varies with time. While online hate speech reduces the ability of alreadymarginalised groups to participate in discussion freely, offline hate speechleads to hate crimes and violence against individuals and communities. Themultifaceted nature of hate speech and its real-world impact have alreadypiqued the interest of the data mining and machine learning communities.Despite our best efforts, hate speech remains an evasive issue for researchersand practitioners alike. This article presents methodological challenges thathinder building automated hate mitigation systems. These challenges inspiredour work in the broader area of combating hateful content on the web. Wediscuss a series of our proposed solutions to limit the spread of hate speechon social media.

Nipping in the Bud: Detection, Diffusion and Mitigation of Hate Speech on Social Media