The rapid increase in hate speech on social media has exposed an unprecedented impact on society, making automated methods for detecting such content important. Unlike prior black-box models, we propose a novel transparent method for automated hate and counter speech recognition, i.e., “Speech Concept Bottleneck Model” (SCBM), using adjectives as human-interpretable bottleneck concepts. SCBM leverages large language models (LLMs) to map input texts to an abstract adjective-based representation, which is then sent to a light-weight classifier for downstream tasks. Across five benchmark datasets spanning multiple languages and platforms (e.g., Twitter, Reddit, YouTube), SCBM achieves an average macro-F1 score of 0.69 which outperforms the most recently reported results from the literature on four out of five datasets. Aside from high recognition accuracy, SCBM provides a high level of both local and global interpretability. Furthermore, fusing our adjective-based concept representation with transformer embeddings, leads to a 1.8% performance increase on average across all datasets, showing that the proposed representation captures complementary information. Our results demonstrate that adjective-based concept representations can serve as compact, interpretable, and effective encodings for hate and counter speech recognition. With adapted adjectives, our method can also be applied to other NLP tasks.

We present CounterHelp, a mobile app designed to support adolescents and young people in generating customized counter speech in response to hateful comments on TikTok. CounterHelp allows users to specify counter strategies to combat hate speech. After users share a hateful comment with CounterHelp, it retrieves TikTok metadata to capture its context. Leveraging large language models, CounterHelp generates customised and context-sensitive counter speech in one of four predefined styles, particularly tailored for adolescents. A user experience lab study confirms the effectiveness and usability of CounterHelp and the generated counter speech.