arXiv AI

Hate Speech Detection in Turkish and Arabic: A Comprehensive Study

arXiv:2607. 00143v2 Announce Type: replace-cross Abstract: Online hate speech has been linked to a global rise in violence against minorities, including incidents such as mass shootings, lynchings, and ethnic cleansing.

Hugging Face Trending Papers
Jul 29

AHA-Memes: A Fine-Grained Multimodal Benchmark for Understanding Hate in Arabic Memes

Hateful memes are a growing form of multimodal online harm, where hostile intent is often conveyed through the joint interpretation of images, text, cultural references, and implicit targets. While hateful meme detection has advanced in high-resource languages, Arabic remains underexplored, with existing meme resources focusing mainly on propaganda or coarse harmful-content labels.

arXiv Machine Learning
Jun 9

Measuring a hate speech spectrum with faceted Rasch item response theory and perspective-aware, explainable-by-design deep learning

arXiv:2009. 10277v2 Announce Type: replace-cross Abstract: We propose a system for measuring hate speech on a continuous, interval-valued spectrum ranging from genocidal to supportive speech by combining supervised deep learning with faceted Rasch item response theory (IRT).

By Chris J. Kennedy, Geoff Bacon, Alexander Sahn, Claudia von Vacano
arXiv AI
Jun 12

Mod-Guide: An LLM-based Content Moderation Feedback System to Address Insensitive Speech toward Indigenous Ethnic and Religious Minority Communities

arXiv:2606. 13397v1 Announce Type: cross Abstract: Language operates as a mechanism of both marginalization and resistance, especially for minority communities navigating insensitive and harmful speech online.

By Dipto Das, Achhiya Sultana, Ankit Singh Chauhan, Saadia Binte Alam, Mohammad Shidujaman, Shion Guha, Sunandan Chakraborty, Syed Ishtiaque Ahmed
arXiv AI
Jul 21

Posts of Peril: Detecting Information About Hazards in Text

arXiv:2405. 17838v3 Announce Type: replace-cross Abstract: Socio-linguistic indicators of affectively-relevant phenomena, such as emotion or sentiment, are often extracted from text to better understand features of human-computer interactions, including on social media.

By Keith Burghardt, Daniel M. T. Fessler, Chyna Tang, Anne Pisor, Kristina Lerman