natural-language-processing-3

Abusive Language

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

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Dataset

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Libraries

i

Use these libraries to find abusive-language-3 models and implementations

Datasets

Hate Speech

Hate Speech and Offensive Language

Torque

TalkDown

DKhate

PointDenoisingBenchmark

Subtasks

No subtasks available.

Most implemented papers

Comparative Studies of Detecting Abusive Language on Twitter

Seunghyun Yoon, Kyomin Jung, Younghun Lee•Wed Aug 29 2018

This paper conducts the first comparative study of various learning models on Hate and Abusive Speech on Twitter, and shows that bidirectional GRU networks trained on word-level features, with Latent Topic Clustering modules, is the most accurate model.

81

Content

The ComMA Dataset v0.2

CoRAL dataset

Cards Against Humanity

0

Paper Graph

Racial Bias in Hate Speech and Abusive Language Detection Datasets

Ingmar Weber, Thomas Davidson, Debasmita Bhattacharya•Tue Apr 30 2019

Evidence of systematic racial bias in five different sets of Twitter data annotated for hate speech and abusive language is examined, as classifiers trained on them tend to predict that tweets written in African-American English are abusive at substantially higher rates.

507 0

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One-step and Two-step Classification for Abusive Language Detection on Twitter

Ji Ho Park, Pascale Fung•Wed May 31 2017

This research explores a two- step approach of performing classification on abusive language and then classifying into specific types and compares it with one-step approach of doing one multi-class classification for detecting sexist and racist languages.

375 0

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Understanding Abuse: A Typology of Abusive Language Detection Subtasks

Zeerak Talat, Ingmar Weber, Thomas Davidson, Dana Warmsley•Sat May 27 2017

A typology that captures central similarities and differences between subtasks is proposed and the implications of this for data annotation and feature construction are discussed.

505 0

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Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training

Chien-Sheng Wu, Andrea Madotto, Peng Xu, Ji Ho Park, Pascale Fung•Fri Aug 31 2018

Emo2Vec is proposed which encodes emotional semantics into vectors and outperforms existing affect-related representations, such as Sentiment-Specific Word Embedding and DeepMoji embeddings with much smaller training corpora.

74 0

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Sequence Classification with Human Attention

Anders Søgaard, Joachim Bingel, Marek Rei, Nora Hollenstein, Maria Barrett•Sun Sep 30 2018

Estimated human attention derived from eye-tracking corpora is used to regularize attention functions in recurrent neural networks and shows substantial improvements across a range of tasks, including sentiment analysis, grammatical error detection, and detection of abusive language.

100 0

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Stop PropagHate at SemEval-2019 Tasks 5 and 6: Are abusive language classification results reproducible?

Paula Fortuna, Sérgio Nunes, Juan Soler•Fri May 31 2019

This paper summarizes the participation of Stop PropagHate team at SemEval 2019, finding poor results when applying it to the HatEval contest, but proving to have a better performance for offense detection than for hate speech.

6 0

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Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter

Muhammad Okky Ibrohim, I. Budi•Mon Dec 31 2018

This research discusses multi-label text classification for abusive language and hate speech detection including detecting the target, category, and level of hate speech in Indonesian Twitter using machine learning approach with Support Vector Machine, Naive Bayes, and Random Forest Decision Tree methods.

232 0

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