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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 51–73 of 73 papers

TitleStatusHype
Online abuse detection: the value of preprocessing and neural attention modelsCode0
Racial Bias in Hate Speech and Abusive Language Detection DatasetsCode0
Abusive Language Detection in Online Conversations by Combining Content-and Graph-based FeaturesCode0
UM-IU@LING at SemEval-2019 Task 6: Identifying Offensive Tweets Using BERT and SVMsCode0
Abusive Language Detection with Graph Convolutional Networks—0
Offensive Language Analysis using Deep Learning ArchitectureCode0
Conversational Networks for Automatic Online Moderation—0
Did you offend me? Classification of Offensive Tweets in Hinglish LanguageCode0
Determining Code Words in Euphemistic Hate Speech Using Word Embedding Networks—0
Aggressive language in an online hacking forum—0
Context-Aware Attention for Understanding Twitter Abuse—0
Mind Your Language: Abuse and Offense Detection for Code-Switched Languages—0
Neural Character-based Composition Models for Abuse Detection—0
Comparative Studies of Detecting Abusive Language on TwitterCode1
Author Profiling for Abuse DetectionCode0
Language Identification and Named Entity Recognition in Hinglish Code Mixed Tweets—0
A Unified Deep Learning Architecture for Abuse Detection—0
Detecting Offensive Language in Tweets Using Deep LearningCode0
Graph-based Features for Automatic Online Abuse Detection—0
One-step and Two-step Classification for Abusive Language Detection on TwitterCode1
Understanding Abuse: A Typology of Abusive Language Detection SubtasksCode0
Impact Of Content Features For Automatic Online Abuse Detection—0
Throttling Poisson Processes—0
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