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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 21–30 of 73 papers

TitleStatusHype
The Online Behaviour of the Algerian Abusers in Social Media Networks—0
Entropy-based Attention Regularization Frees Unintended Bias Mitigation from ListsCode1
Abuse and Fraud Detection in Streaming Services Using Heuristic-Aware Machine Learning—0
ADIMA: Abuse Detection In Multilingual AudioCode0
Identifying Adversarial Attacks on Text Classifiers—0
Toxicity Detection for Indic Multilingual Social Media Content—0
Improving Generalizability in Implicitly Abusive Language Detection with Concept Activation Vectors—0
What Models Know About Their Attackers: Deriving Attacker Information From Latent Representations—0
ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Abuse Detection in Conversational AICode1
A Large-Scale English Multi-Label Twitter Dataset for Cyberbullying and Online Abuse Detection—0
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