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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
KUISAIL at SemEval-2020 Task 12: BERT-CNN for Offensive Speech Identification in Social MediaCode1
One-step and Two-step Classification for Abusive Language Detection on TwitterCode1
ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Abuse Detection in Conversational AICode1
Intersectional Bias in Hate Speech and Abusive Language DatasetsCode1
Kungfupanda at SemEval-2020 Task 12: BERT-Based Multi-Task Learning for Offensive Language DetectionCode1
Multimodal Meme Dataset (MultiOFF) for Identifying Offensive Content in Image and TextCode1
AbuseAnalyzer: Abuse Detection, Severity and Target Prediction for Gab PostsCode1
Comparative Studies of Detecting Abusive Language on TwitterCode1
Entropy-based Attention Regularization Frees Unintended Bias Mitigation from ListsCode1
Adversarial Robustness for Tabular Data through Cost and Utility Awareness—0
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