SOTAVerified

Hate Speech Detection

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.

Papers

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Showing 1–10 of 507 papers

TitleStatusHype
Fine-Grained Chinese Hate Speech Understanding: Span-Level Resources, Coded Term Lexicon, and Enhanced Detection Frameworks—0
Rethinking Hate Speech Detection on Social Media: Can LLMs Replace Traditional Models?—0
Towards Fairness Assessment of Dutch Hate Speech Detection—0
ToxSyn-PT: A Large-Scale Synthetic Dataset for Hate Speech Detection in Portuguese—0
Hateful Person or Hateful Model? Investigating the Role of Personas in Hate Speech Detection by Large Language Models—0
Multilingual Hate Speech Detection in Social Media Using Translation-Based Approaches with Large Language Models—0
Cracking the Code: Enhancing Implicit Hate Speech Detection through Coding Classification—0
On Fairness of Task Arithmetic: The Role of Task Vectors—0
AmpleHate: Amplifying the Attention for Versatile Implicit Hate DetectionCode0
Social Good or Scientific Curiosity? Uncovering the Research Framing Behind NLP Artefacts—0
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All datasetsEthos BinaryHateXplainEthos MultiLabelWaseem et al., 2018AbusEvalAutomatic Misogynistic IdentificationHateMMHatEvalOffensEval 2019ToLD-Brbajer_danish_misogynyDKhate

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MLARAMHamming Loss0.29—Unverified
2MLkNNHamming Loss0.16—Unverified
3Binary RelevanceHamming Loss0.14—Unverified
4Neural Classifier ChainsHamming Loss0.13—Unverified
5Neural Binary RelevanceHamming Loss0.11—Unverified