SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–10 of 73 papers

TitleStatusHype
Entropy-based Attention Regularization Frees Unintended Bias Mitigation from ListsCode1
ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Abuse Detection in Conversational AICode1
AbuseAnalyzer: Abuse Detection, Severity and Target Prediction for Gab PostsCode1
KUISAIL at SemEval-2020 Task 12: BERT-CNN for Offensive Speech Identification in Social MediaCode1
Intersectional Bias in Hate Speech and Abusive Language DatasetsCode1
Multimodal Meme Dataset (MultiOFF) for Identifying Offensive Content in Image and TextCode1
Kungfupanda at SemEval-2020 Task 12: BERT-Based Multi-Task Learning for Offensive Language DetectionCode1
Comparative Studies of Detecting Abusive Language on TwitterCode1
One-step and Two-step Classification for Abusive Language Detection on TwitterCode1
Creating and Evaluating Code-Mixed Nepali-English and Telugu-English Datasets for Abusive Language Detection Using Traditional and Deep Learning Models—0
Show:102550
← PrevPage 1 of 8Next →

No leaderboard results yet.