Abuse Detection
29 papers with code • 0 benchmarks • 4 datasets
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).
Benchmarks
These leaderboards are used to track progress in Abuse Detection
Hate Speech and Offensive Language
Most implemented papers
Racial Bias in Hate Speech and Abusive Language Detection Datasets
Technologies for abusive language detection are being developed and applied with little consideration of their potential biases.
Comparative Studies of Detecting Abusive Language on Twitter
younggns/comparative-abusive-lang
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WS 2018
However, this dataset has not been comprehensively studied to its potential.
Understanding Abuse: A Typology of Abusive Language Detection Subtasks
As the body of research on abusive language detection and analysis grows, there is a need for critical consideration of the relationships between different subtasks that have been grouped under this label.
One-step and Two-step Classification for Abusive Language Detection on Twitter
younggns/comparative-abusive-lang
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WS 2017
Automatic abusive language detection is a difficult but important task for online social media.
Detecting Offensive Language in Tweets Using Deep Learning
This paper addresses the important problem of discerning hateful content in social media.
Author Profiling for Abuse Detection
The rapid growth of social media in recent years has fed into some highly undesirable phenomena such as proliferation of hateful and offensive language on the Internet.
Did you offend me? Classification of Offensive Tweets in Hinglish Language
The use of code-switched languages (\textit{e. g.}, Hinglish, which is derived by the blending of Hindi with the English language) is getting much popular on Twitter due to their ease of communication in native languages.
Offensive Language Analysis using Deep Learning Architecture
RyanOngAI/semeval-2019-task6
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Once we are happy with the quality of our input data, we proceed to choosing the optimal deep learning architecture for this task.
UM-IU@LING at SemEval-2019 Task 6: Identifying Offensive Tweets Using BERT and SVMs
zytian9/SemEval-2019-Task-6
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SEMEVAL 2019
This paper describes the UM-IU@LING's system for the SemEval 2019 Task 6: OffensEval.
Abusive Language Detection in Online Conversations by Combining Content-and Graph-based Features
In recent years, online social networks have allowed worldwide users to meet and discuss.
