In this paper, we first provide an in-depth and systematic analysis of 7 standard benchmarks for HS detection, relying on a fine-grained and linguistically-grounded definition of implicit and subtle messages. Then, we experiment with state-of-the-art neural network architectures on two supervised tasks, namely implicit HS and subtle HS message classification. We show that while such models perform satisfactory on explicit messages, they fail to detect implicit and subtle content, highlighting the fact that HS detection is not a solved problem and deserves further investigation.https://aclanthology.org/2023.eacl-main.147/Share this:FacebookXLike this:Like Loading... Post navigation What Did You Learn To Hate? A Topic-Oriented Analysis of Generalization in Hate Speech Detection (ACL Anthology) Hate speech and abusive language detection in Indonesian social media: Progress and challenges (Heliyon)