BIDS co-invests in this project with the D-Lab, UC Berkeley’s home for data science training in the humanities and social sciences, to support the development of natural language processing (NLP) and machine learning (ML) tools for the scalable detection of online hate speech.
Proactive moderation of hate speech and abuse in online communities can affect substantial changes in online environments. Encountering hate on the internet has become a routine part of the online experience for many. According to the Pew Research Center, 41% of American adults have experienced online harassment, and 66% have witnessed it. For those on the receiving end of online vitriol and bigotry, there is no mistaking what is happening: these are words that wound, which are often defined by recipients as hate speech.
The Online Hate Index (OHI), a joint initiative of UC Berkeley’s D-Lab and the Anti-Defamation League, and in collaboration with Reddit, Google, and Facebook, will transform our understanding of hate speech into a scalable tool that can be deployed on internet content to discover the scope and spread of online hate speech. Through a continual process of machine learning, based on a labeling protocol developed by D-Lab and implemented by a team of human coders, this tool will uncover trends across different online platforms, allowing us to push for the technical, legal, social, and policy changes necessary to ensure that online communities are safe and inclusive spaces.
