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SotA/Natural Language Processing/Hate Speech Detection

Hate Speech Detection

23 benchmarks507 papers

Hate speech detection is the task of detecting if communication such as text, audio, and so on contains hatred and or encourages violence towards a person or a group of people. This is usually based on prejudice against 'protected characteristics' such as their ethnicity, gender, sexual orientation, religion, age et al. Some example benchmarks are ETHOS and HateXplain. Models can be evaluated with metrics like the F-score or F-measure.

Benchmarks

Hate Speech Detection on Ethos Binary

F1-scoreClassification AccuracyPrecision

Hate Speech Detection on HateXplain

AUROCAccuracyMacro F1Macro-F1

Hate Speech Detection on HopeEDI

Weighted Average F1-score

Hate Speech Detection on Ethos MultiLabel

Hamming Loss

Hate Speech Detection on Waseem et al., 2018

AAAF1 (micro)

Hate Speech Detection on AbusEval

Macro F1

Hate Speech Detection on Automatic Misogynistic Identification

Accuracy

Hate Speech Detection on HatEval

Macro F1

Hate Speech Detection on HateMM

TEST F1 (macro)

Hate Speech Detection on OffensEval 2019

Macro F1

Hate Speech Detection on ToLD-Br

F1-score

Hate Speech Detection on DKhate

F1

Hate Speech Detection on Hostility Detection Dataset in Hindi

F1 score

Hate Speech Detection on KanHope

F1-score (Weighted)

Hate Speech Detection on OLID

Macro F1

Hate Speech Detection on SHAJ

F1

Hate Speech Detection on bajer_danish_misogyny

F1