The paper introduces a scope‑conditioned generation framework that incorporates structured stereotype characteristics into prompts for large language models, aiming to improve the quality of counterspeech against online hate speech. The authors validate the method on a new, human‑curated dataset in English, Italian, and Spanish, showing significant gains over generic baselines in factuality, specificity, cogency, and effectiveness for both explicit and implicit stereotypes.
By Greta Damo, Elias Urios Alacreu, Elena Cabrio, Paolo Rosso, Serena Villata
arXiv:2608.23152v1 Announce Type: new
Abstract: Counterspeech effectively neutralizes the impact of online hate. Although prior work explores automated counterspeech generation, it largely emphasizes...
By Sujoy Nath, Aswini Kumar, Tanmoy Chakraborty
The paper introduces WSF-ARG+, a new dataset that pairs hate speech with check‑worthiness annotations, and presents an LLM‑in‑the‑loop framework to streamline the annotation process. Experiments with 12 open‑weight large language models demonstrate that the framework cuts human effort while maintaining annotation quality. The study also shows that incorporating check‑worthiness labels improves hate‑speech detection performance, boosting macro‑F1 scores for large models by up to 0.213 and averaging 0.154 across models.
By Nicol\'as Benjam\'in Ocampo, Tommaso Caselli, Davide Ceolin
The paper proposes a training‑time explainability framework that aligns model reasoning with human‑annotated rationales to improve both classification performance and interpretability for multilingual hate speech detection. It is evaluated on HateXplain (English) and BullySent (Hinglish), datasets that capture anti‑Muslim hate in culturally coded, multilingual forms. Using methods such as LIME, Integrated Gradients, Grad‑X‑Input, and attention, the study shows that gradient‑ and attention‑based regularization boosts F‑scores, enhances plausibility and faithfulness, and captures culturally specific cues for detecting implicit anti‑Muslim hate.
By Muhammad Deedahwar Mazhar Qureshi, Sannaan Khan, Muhammad Atif Qureshi, Wael Rashwan
Large language models (LLMs) demonstrate impressive performance across a wide range of general NLP tasks; however, their effectiveness in sensitive domains, such as hate speech detection, remains less...
arXiv:2510. 12857v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are now widely deployed in user-facing applications, reaching hundreds of millions of users worldwide.
By Robin Staab, Jasper Dekoninck, Maximilian Baader, Martin Vechev
The paper presents an instruction‑tuned large language model (LLM) based on Qwen3 that is fine‑tuned for hate speech mitigation by unifying 36 English hate speech datasets. The authors show that this generalist LLM achieves state‑of‑the‑art performance on in‑domain benchmarks and delivers significant gains in cross‑domain and cross‑lingual generalization, outperforming specialist encoder‑based classifiers.
By Lukas Edman, Daryna Dementieva, Alexander Fraser
The paper introduces FAID, a fine‑grained adaptive framework for detecting implicit hate speech. It first classifies samples into Shallow, Targeted, or Context‑Dependent categories and then applies tailored strategies—prompt‑tuning for shallow cases, knowledge augmentation for targeted ones, and an agentic prompt‑generation system for context‑dependent posts. Experiments on four benchmark datasets show that FAID outperforms state‑of‑the‑art baselines by allocating computational effort only where needed.
By Han Wang, Yuhu Cheng, Xuesong Wang, Yi Zhu
arXiv:2608.22018v1 Announce Type: new
Abstract: Hate speech detection has recently shifted from coarse-grained classification to structured parsing, where systems must jointly identify hateful target...
By Yifan Lyu, Dianqing Lin, Xinran Li, Jiaqi Qiao, Xiujuan Xu
MexHat is a newly released video dataset aimed at improving hate‑speech detection in Mexican Spanish. It contains roughly 1,000 clips annotated for three broad categories—no negative content, offensive content, and hate‑speech—as well as a finer classification into three hate‑speech sub‑categories. The paper presents dataset statistics and baseline results, underscoring the challenges of detecting culturally and contextually nuanced hate speech in multimodal content.
By Itzel Tlelo-Coyotecatl, Hugo Jair Escalante
arXiv:2606. 30989v1 Announce Type: cross Abstract: Warning: This paper contains several toxic and offensive statements.
By Naihao Deng, Yilun Zhu, Joan Nwatu, Clayton Scott, Rada Mihalcea
arXiv:2609.16393v1 Announce Type: new
Abstract: We introduce ParsHate, a manually annotated dataset of 10,000 Persian tweets spanning 2013-2022, representing the first decade-long benchmark for hate...
By Zahra Bokaei, Walid Magdy, Bonnie Webber