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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

Showing 3140 of 73 papers

TitleStatusHype
Identifying Adversarial Attacks on Text Classifiers0
Impact Of Content Features For Automatic Online Abuse Detection0
Improving Generalizability in Implicitly Abusive Language Detection with Concept Activation Vectors0
Joint Modelling of Emotion and Abusive Language Detection0
Language Identification and Named Entity Recognition in Hinglish Code Mixed Tweets0
LIIR at SemEval-2020 Task 12: A Cross-Lingual Augmentation Approach for Multilingual Offensive Language Identification0
Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods0
Mind Your Language: Abuse and Offense Detection for Code-Switched Languages0
Multilingual and Multimodal Abuse Detection0
Neural Character-based Composition Models for Abuse Detection0
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