Countering Hate Speech: Modeling User-Generated Web Content Using Natural Language Processing

Social media is considered a particularly conducive arena for hate speech. Counter speech, which is a "direct response that counters hate speech" is a remedy to address hate speech. Unlike content moderation, counter speech does not interfere with the principle of free and open public spaces for debate. This dissertation focuses on the (a) automatic detection and (b) analyses of the effectiveness of counter speech and its fine-grained strategies in user-generated web content. The first goal is to identify counter speech. We create a corpus with 6,846 instances through crowdsourcing. We specifically investigate the role of conversational context in the annotation and detection of counter speech. The second goal is to assess and predict conversational outcomes of counter speech. We propose a new metric to measure conversation incivility based on the number of uncivil and civil comments as well as the unique authors involved in the discourse. We then use the metric to evaluate the outcomes of replies to hate speech. The third goal is to establish a fine-grained taxonomy of counter speech. We present a theoretically grounded taxonomy that differentiates counter speech addressing the author of hate speech from addressing the content. We further compare the conversational outcomes of different types of counter speech and build models to identify each type. We conclude by discussing our contributions and future research directions on using user-generated counter speech to combat online hatred