arXiv:2609.13579v1 Announce Type: new
Abstract: Safety research often focuses on model-generated harms, but users may also direct hostility, coercion, and adversarial pressure at models. Understandin...
By Fanqi Zeng, Sadid A. Hasan, Chaocheng He
arXiv:2606. 28772v1 Announce Type: cross Abstract: Hate speech annotation pipelines routinely collapse annotator disagreement into majority vote labels before training.
By Joshua Muhumuza, Joab Ezra Agaba, Mercy Amiyo
The study introduces a unified method for measuring rhetorical distortion across six social media platforms and two languages, distinguishing between creator posts and their replies. It identifies five distortion dimensions—Significance Inflation, Anxiety Manufacturing, Novelty Claims, Loaded Language, and Temporal Distortion—using a three‑tier pipeline that combines lexical rules, negative filters, and LLM verification. Results show that distortion is far more strongly linked to the platform than content type, with X/Twitter exhibiting the highest distortion scores, and that the audience layer (replies) carries significantly more distortion than creator posts.
By Xucheng Yu, Jiangping Chen, Haohan Wang
The study investigates whether large language models (LLMs) rely on textual evidence or on their pre‑existing knowledge when annotating text. Using counterfactual prompts for 995 public figures, the authors find that LLMs often default to memory shortcuts—providing correct facts about a figure even when the text contains no supporting evidence. These shortcuts are amplified by evaluation design and incentives, but can be mitigated by name reminders or masking, though with trade‑offs in gender annotation.
By Wenchang Wang, Yue Song
CST is a lightweight risk triage system that provides moderators with source‑grounded evidence for web discussions. It first assigns risk scores using character patterns and a multilingual model, then groups related messages and selects key excerpts to show, linking them back to the original thread. Across multiple languages and datasets, CST outperforms fully fine‑tuned models in ranking harmful messages and reduces moderator review time by at least 27% while maintaining high accuracy.
By Zhihua Wang, Yifeng Gao, Yunhao Feng, Rui Pan, Baiheng Zhao, Zixing Chen, Nan Li
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