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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 1–25 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
Multimodal Meme Dataset (MultiOFF) for Identifying Offensive Content in Image and TextCode1
ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Abuse Detection in Conversational AICode1
Comparative Studies of Detecting Abusive Language on TwitterCode1
AbuseAnalyzer: Abuse Detection, Severity and Target Prediction for Gab PostsCode1
Intersectional Bias in Hate Speech and Abusive Language DatasetsCode1
Kungfupanda at SemEval-2020 Task 12: BERT-Based Multi-Task Learning for Offensive Language DetectionCode1
Entropy-based Attention Regularization Frees Unintended Bias Mitigation from ListsCode1
Generalisability of Topic Models in Cross-corpora Abusive Language Detection—0
Enriching Abusive Language Detection with Community Context—0
Abuse and Fraud Detection in Streaming Services Using Heuristic-Aware Machine Learning—0
Adversarial Robustness for Tabular Data through Cost and Utility Awareness—0
CoLLAB: A Collaborative Approach for Multilingual Abuse Detection—0
DetoxBench: Benchmarking Large Language Models for Multitask Fraud & Abuse Detection—0
Graph-based Features for Automatic Online Abuse Detection—0
A Unified Deep Learning Architecture for Abuse Detection—0
Creating and Evaluating Code-Mixed Nepali-English and Telugu-English Datasets for Abusive Language Detection Using Traditional and Deep Learning Models—0
Conversational Networks for Automatic Online Moderation—0
A survey of textual cyber abuse detection using cutting-edge language models and large language models—0
Darkness can not drive out darkness: Investigating Bias in Hate SpeechDetection Models—0
DE-ABUSE@TamilNLP-ACL 2022: Transliteration as Data Augmentation for Abuse Detection in Tamil—0
Abusive Language Detection with Graph Convolutional Networks—0
Detection of Children Abuse by Voice and Audio Classification by Short-Time Fourier Transform Machine Learning implemented on Nvidia Edge GPU device—0
Context-Aware Attention for Understanding Twitter Abuse—0
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