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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 41–50 of 73 papers

TitleStatusHype
Voucher Abuse Detection with Prompt-based Fine-tuning on Graph Neural Networks—0
What Models Know About Their Attackers: Deriving Attacker Information From Latent Representations—0
Abuse and Fraud Detection in Streaming Services Using Heuristic-Aware Machine Learning—0
Abusive Language Detection with Graph Convolutional Networks—0
Adversarial Robustness for Tabular Data through Cost and Utility Awareness—0
Aggressive language in an online hacking forum—0
A Large-Scale English Multi-Label Twitter Dataset for Cyberbullying and Online Abuse Detection—0
A survey of textual cyber abuse detection using cutting-edge language models and large language models—0
A Unified Deep Learning Architecture for Abuse Detection—0
CoLLAB: A Collaborative Approach for Multilingual Abuse Detection—0
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