  Counterspeech offers direct rebuttals to hateful speech by challenging
perpetrators of hate and showing support to targets of abuse. It provides a
promising alternative to more contentious measures, such as content moderation
and deplatforming, by contributing a greater amount of positive online speech
rather than attempting to mitigate harmful content through removal. Advances in
the development of large language models mean that the process of producing
counterspeech could be made more efficient by automating its generation, which
would enable large-scale online campaigns. However, we currently lack a
systematic understanding of several important factors relating to the efficacy
of counterspeech for hate mitigation, such as which types of counterspeech are
most effective, what are the optimal conditions for implementation, and which
specific effects of hate it can best ameliorate. This paper aims to fill this
gap by systematically reviewing counterspeech research in the social sciences
and comparing methodologies and findings with computer science efforts in
automatic counterspeech generation. By taking this multi-disciplinary view, we
identify promising future directions in both fields.
