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Abuse Detection

Abuse detection is the task of identifying abusive behaviors, such as hate speech, offensive language, sexism and racism, in utterances from social media platforms (Source: https://arxiv.org/abs/1802.00385).

Papers

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Showing 1–10 of 73 papers

TitleStatusHype
Creating and Evaluating Code-Mixed Nepali-English and Telugu-English Datasets for Abusive Language Detection Using Traditional and Deep Learning Models—0
Predictive Response Optimization: Using Reinforcement Learning to Fight Online Social Network Abuse—0
A survey of textual cyber abuse detection using cutting-edge language models and large language models—0
HP-BERT: A framework for longitudinal study of Hinduphobia on social media via LLMsCode0
Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings with Few-Shot LearningCode0
DetoxBench: Benchmarking Large Language Models for Multitask Fraud & Abuse Detection—0
CoLLAB: A Collaborative Approach for Multilingual Abuse Detection—0
Breaking the Silence Detecting and Mitigating Gendered Abuse in Hindi, Tamil, and Indian English Online SpacesCode0
Overview of the 2023 ICON Shared Task on Gendered Abuse Detection in Indic Languages—0
Voucher Abuse Detection with Prompt-based Fine-tuning on Graph Neural Networks—0
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