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Counterspeech, i.e., responses to counteract potential harms of hatefulspeech, has become an increasingly popular solution to address online hatespeech without censorship. However, properly countering hateful languagerequires countering and dispelling the underlying inaccurate stereotypesimplied by such language. In this work, we draw from psychology and philosophyliterature to craft six psychologically inspired strategies to challenge theunderlying stereotypical implications of hateful language. We first examine theconvincingness of each of these strategies through a user study, and thencompare their usages in both human- and machine-generated counterspeechdatasets. Our results show that human-written counterspeech uses counteringstrategies that are more specific to the implied stereotype (e.g., counterexamples to the stereotype, external factors about the stereotype's origins),whereas machine-generated counterspeech uses less specific strategies (e.g.,generally denouncing the hatefulness of speech). Furthermore, machine-generatedcounterspeech often employs strategies that humans deem less convincingcompared to human-produced counterspeech. Our findings point to the importanceof accounting for the underlying stereotypical implications of speech whengenerating counterspeech and for better machine reasoning aboutanti-stereotypical examples.

Beyond Denouncing Hate: Strategies for Countering Implied Biases and Stereotypes in Language