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
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
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...
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
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
We introduce ParsHate, a manually annotated dataset of 10,000 Persian tweets spanning 2013-2022, representing the first decade-long benchmark for hate speech detection in Persian. The dataset contains...
The paper evaluates large language models for hate‑speech detection in Roman Urdu, a low‑resource language with informal spelling variations. Using the Parameter‑Efficient Fine‑Tuning technique Low‑Rank Adaptation (LoRA), the authors fine‑tune models such as Mistral, LLaMA, Falcon, and multilingual BERT on the 72,000‑comment PURUTT dataset. While zero‑shot performance yields an F1 of 0.56, fine‑tuning a small fraction of parameters boosts F1 scores above 0.93, demonstrating that PEFT offers both high accuracy and computational efficiency for low‑resource language tasks.
By Toneema Zubair, Muhammad Junaid Asif, Faisal Kamiran, Hafiz Hassan Saeed, Rana Fayyaz Ahmad
The paper introduces SAGE, a lightweight linguistic framework that detects AI-generated peer reviews by exploiting subjectivity at semantic and pragmatic levels. It uses speech act annotations and model-predicted subjectivity scores to train a classifier achieving an AUROC of 0.9566. When combined with the Sem-Detect detector, SAGE boosts the true positive rate at 1% false positive rate on the APRDB benchmark from 0.874 to 0.985, surpassing current state‑of‑the‑art methods.
By Gonzalo Freijedo Aduna (IJN), Charles Yaiche (CRIStAL), Patrick Bas (CRIStAL), Alda Mari (IJN)
The paper presents an explainable hate‑speech detection framework that combines DistilBERT embeddings, a Bi‑LSTM network, and an attention mechanism to capture contextual and sequential information. It uses LIME to highlight influential text features, providing transparency in predictions. Evaluated on two benchmark datasets for both binary and multi‑class tasks, the model achieves F1‑scores of 96.78%–99.53% for binary classification and 94.99%–97.00% for multi‑class classification, outperforming existing baselines.
By Rameesha Zia, Muhammad Shahid Iqbal Malik
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:2610.27285v1 Announce Type: cross
Abstract: Effective detection of hate content is essential for maintaining healthy online communities, yet performance can vary substantially across languages....
By Zahra Bokaei, Walid Magdy, Bonnie Webber
arXiv:2610.16957v1 Announce Type: cross
Abstract: Standard hate speech detection datasets often emphasize explicit harmful language, but directed harm can also be expressed indirectly, without clearl...
By Niyazi Ahmet Metin, Dilara Kek\"ull\"uo\u{g}lu