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 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:2609.14178v1 Announce Type: new
Abstract: The rapid diffusion of hate speech and misinformation on social networks challenges democratic societies, since direct suppression efforts may deepen p...
By Carmel Kronfeld, Sharva Gogawale, Tetsuro Kobayashi, Irad Ben-Gal
The paper investigates how annotator-style rebuttals can manipulate large language model (LLM) moderation systems, either by whitewashing hateful content as normal or smearing normal content as hateful. Using a rejudge protocol that adds decision‑boundary perturbations and adversarial rationales, the authors show that such rebuttals significantly degrade moderation performance, especially in multi‑turn settings. The study finds consistent, model‑specific asymmetries between the two manipulation directions and demonstrates that explicit reasoning prompts and defensive instructions mitigate but do not eliminate the vulnerability.
By Junyu Lu, Kaiyuan Liu, Jingyi Kang, Deyi Ji, Hailong Zhang, Lanyun Zhu, Qi Zhu, Bo Xu, Liang Yang, Hongfei Lin
arXiv:2607. 20447v1 Announce Type: cross Abstract: This paper describes our system for the EEUCA 2026 Shared Task on toxicity classification in gaming chat.
By Anmol Guragain, Marcos Estecha-Garitagoitia, Luis Fernando D'Haro Enr\'iquez, Ricardo de C\'ordoba
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
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:2601. 11178v3 Announce Type: replace Abstract: Social media platforms are increasingly dominated by long-form multimodal content, where harmful narratives are constructed through a complex interplay of audio, visual, and textual cues.
By Girish A. Koushik, Helen Treharne, Diptesh Kanojia
The paper introduces ProKDA, a progressive knowledge-to-decision alignment framework for explainable hateful meme detection. ProKDA separates explanation generation and label prediction into three sequential training stages—background knowledge learning, hatefulness detection learning, and hatefulness boundary alignment—reducing task interference. Experiments on three public benchmarks demonstrate that ProKDA achieves state‑of‑the‑art detection performance while providing accurate, evidence‑supported explanations for moderation decisions.
By Bo Xu, Chenyuan Wang, Xinyu Chen, Quanhao Zhu, Rui Lin, Liang Zhao, Hongfei Lin, Feng Xia
Counterspeech (CS) - direct responses that counter online Hate Speech (HS) using reasoning and alternative viewpoints - has emerged as an alternative to content removal. Current automatic CS generatio...
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
The paper presents an agentic framework for detecting conspiratorial content in social media by inferring the speaker’s intent rather than merely identifying explicit claims. It leverages social context and adaptive tool use, demonstrating superior performance over text-only and non-agentic models on a large Hebrew tweet dataset spanning election cycles and the COVID pandemic. The study highlights the importance of context-aware, reasoning-driven approaches for accurate conspiracy detection.
By Lior Biton, Oren Tsur