Counterspeech on Twitter: A Field Study

This report from our two year study of hateful speech and counterspeech on Twitter reviews existing literature on counterspeech, examines cases of counterspeech through the vector in which it was delivered, and develops a taxonomy of counterspeech strategies.

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Beyond Trump: US Officials Using Hallmarks of Dangerous Speech

Much of the world is transfixed at present by Donald Trump’s often insulting and provocative language. But like any speech, it can only be fully understood in context – which now includes remarks more belligerent and shocking than Trump’s, from his supporters who have shouted “Kill her” and “Build the wall – kill them all” at recent Trump rallies, and from other elected officials such as governors of U.S. states. 

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A Web of Hate: Tackling Hateful Speech in Online Social Spaces

Online social platforms are beset with hateful speech – content that expresses hatred for a person or group of people. Such content can frighten, intimidate, or silence platform users, and some of it can inspire other users to commit violence. Despite widespread recognition of the problems posed by such content, reliable solutions even for detecting hateful speech are lacking. In the present work, we establish why keyword-based methods are insufficient for detection. We then propose an approach to detecting hateful speech that uses content produced by self-identifying hateful communities as training data. Our approach bypasses the expensive annotation process often required to train keyword systems and performs well across several established platforms, making substantial improvements over current state-of-the-art approaches.

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