In this paper, we propose, for the first time, a topic-oriented approach to study generalization across popular hate speech datasets. We first perform a comparative analysis of the performances of Transformer-based models in capturing topic-generic and topic-specific knowledge when trained on different datasets. We then propose a novel, simple yet effective approach to study more precisely which topics are best captured in implicit manifestations of hate, showing that selecting combinations of datasets with better out-of-domain topical coverage improves the reliability of automatic hate speech detection. https://aclanthology.org/2023.eacl-main.254/ Share this: Click to print (Opens in new window) Print Click to share on Facebook (Opens in new window) Facebook Click to share on LinkedIn (Opens in new window) LinkedIn Click to share on Reddit (Opens in new window) Reddit Click to share on WhatsApp (Opens in new window) WhatsApp Click to share on Bluesky (Opens in new window) Bluesky Click to email a link to a friend (Opens in new window) Email Like this:Like Loading... Post navigation An In-depth Analysis of Implicit and Subtle Hate Speech Messages (ACL Anthology) Review on Countering Extremism and Hate Speech. July 2023 (II/II) | Policyinstitute.net