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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 151–200 of 507 papers

TitleStatusHype
BERT-based Ensemble Approaches for Hate Speech Detection—0
Domain Classification-based Source-specific Term Penalization for Domain Adaptation in Hate-speech Detection—0
Effect of Word Embedding Models on Hate and Offensive Speech Detection—0
On Transferability of Bias Mitigation Effects in Language Model Fine-Tuning—0
Do Characters Abuse More Than Words?—0
HateBR: A Large Expert Annotated Corpus of Brazilian Instagram Comments for Offensive Language and Hate Speech Detection—0
Emotion Based Hate Speech Detection using Multimodal Learning—0
BOISHOMMO: Holistic Approach for Bangla Hate Speech—0
An Investigation of Large Language Models for Real-World Hate Speech Detection—0
An Investigation Into Explainable Audio Hate Speech Detection—0
Ensemble of pre-trained language models and data augmentation for hate speech detection from Arabic tweets—0
Digital Guardians: Can GPT-4, Perspective API, and Moderation API reliably detect hate speech in reader comments of German online newspapers?—0
DetoxBench: Benchmarking Large Language Models for Multitask Fraud & Abuse Detection—0
Evaluating ChatGPT's Performance for Multilingual and Emoji-based Hate Speech Detection—0
Evaluating Semantic Feature Representations to Efficiently Detect Hate Intent on Social Media—0
Evaluating Simple Debiasing Techniques in RoBERTa-based Hate Speech Detection Models—0
Determining Code Words in Euphemistic Hate Speech Using Word Embedding Networks—0
Bayesian Methods for Semi-supervised Text Annotation—0
Aggression Detection in Social Media: Using Deep Neural Networks, Data Augmentation, and Pseudo Labeling—0
Explainable and High-Performance Hate and Offensive Speech Detection—0
Explainable Identification of Hate Speech towards Islam using Graph Neural Networks—0
Explaining Hate Speech Classification with Model Agnostic Methods—0
Exploiting Explainability to Design Adversarial Attacks and Evaluate Attack Resilience in Hate-Speech Detection Models—0
Antisemitic Messages? A Guide to High-Quality Annotation and a Labeled Dataset of Tweets—0
HateDebias: On the Diversity and Variability of Hate Speech Debiasing—0
Detection of Hate Speech using BERT and Hate Speech Word Embedding with Deep Model—0
Chain-of-Translation Prompting (CoTR): A Novel Prompting Technique for Low Resource Languages—0
Ceasing hate withMoH: Hate Speech Detection in Hindi-English Code-Switched Language—0
Exploring Large Language Models for Hate Speech Detection in Rioplatense Spanish—0
Exploring new Approaches for Information Retrieval through Natural Language Processing—0
Exploring Stylometric and Emotion-Based Features for Multilingual Cross-Domain Hate Speech Detection—0
Exploring the Adversarial Capabilities of Large Language Models—0
Detecting Optimism in Tweets using Knowledge Distillation and Linguistic Analysis of Optimism—0
CIC at SemEval-2019 Task 5: Simple Yet Very Efficient Approach to Hate Speech Detection, Aggressive Behavior Detection, and Target Classification in Twitter—0
Exposing the limits of Zero-shot Cross-lingual Hate Speech Detection—0
俄语网络仇恨言论语料库研究与构建(An Russian Internet Corpus for Hate Speech Detection)—0
An Interactive Exploratory Tool for the Task of Hate Speech Detection—0
BanTH: A Multi-label Hate Speech Detection Dataset for Transliterated Bangla—0
Detecting Offensive Tweets in Hindi-English Code-Switched Language—0
Fairly Accurate: Learning Optimal Accuracy vs. Fairness Tradeoffs for Hate Speech Detection—0
Code-Mixed Telugu-English Hate Speech Detection—0
Few-shot Hate Speech Detection Based on the MindSpore Framework—0
Generative AI for Hate Speech Detection: Evaluation and Findings—0
Fight Fire with Fire: Fine-tuning Hate Detectors using Large Samples of Generated Hate Speech—0
Fine-Grained Chinese Hate Speech Understanding: Span-Level Resources, Coded Term Lexicon, and Enhanced Detection Frameworks—0
Combating high variance in Data-Scarce Implicit Hate Speech Classification—0
BanglaEmbed: Efficient Sentence Embedding Models for a Low-Resource Language Using Cross-Lingual Distillation Techniques—0
Common Ground, Diverse Roots: The Difficulty of Classifying Common Examples in Spanish Varieties—0
Free speech or Free Hate Speech? Analyzing the Proliferation of Hate Speech in Parler—0
Detecting Hate Speech and Offensive Language on Twitter using Machine Learning: An N-gram and TFIDF based Approach—0
Show:102550
← PrevPage 4 of 11Next →
All datasetsEthos BinaryHateXplainEthos MultiLabelWaseem et al., 2018AbusEvalAutomatic Misogynistic IdentificationHateMMHatEvalOffensEval 2019ToLD-Brbajer_danish_misogynyDKhate

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BiLSTM + static BEF1-score0.8—Unverified
2BERTF1-score0.79—Unverified
3BiLSTM+Attention+FTF1-score0.77—Unverified
4OPT-175B (few-shot)F1-score0.76—Unverified
5CNN+Attention+FT+GVF1-score0.74—Unverified
6OPT-175B (one-shot)F1-score0.71—Unverified
7OPT-175B (zero-shot)F1-score0.67—Unverified
8SVMF1-score0.66—Unverified
9Random ForestsF1-score0.64—Unverified
10Davinci (zero-shot)F1-score0.63—Unverified
#ModelMetricClaimedVerifiedStatus
1BERT-MRPAUROC0.86—Unverified
2BERT-RPAUROC0.85—Unverified
3BERT-HateXplain [Attn]AUROC0.85—Unverified
4BERT-HateXplain [LIME]AUROC0.85—Unverified
5BERT [Attn]AUROC0.84—Unverified
6BiRNN-HateXplain [Attn]AUROC0.81—Unverified
7BiRNN-Attn [Attn]AUROC0.8—Unverified
8CNN-GRU [LIME]AUROC0.79—Unverified
9BiRNN [LIME]AUROC0.77—Unverified
10XG-HSI-BERTAccuracy0.75—Unverified
#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
#ModelMetricClaimedVerifiedStatus
1Mozafari et al., 2019AAA50.94—Unverified
2SVMAAA46.51—Unverified
3Kennedy et al., 2020AAA45.5—Unverified
#ModelMetricClaimedVerifiedStatus
1HateBERTMacro F10.74—Unverified
2BERTMacro F10.72—Unverified
#ModelMetricClaimedVerifiedStatus
1mBertAccuracy0.83—Unverified
2Logistic RegressionAccuracy0.7—Unverified
#ModelMetricClaimedVerifiedStatus
1HXP + CLAP + CLIPTEST F1 (macro)0.85—Unverified
2BERT + ViT + MFCCTEST F1 (macro)0.79—Unverified
#ModelMetricClaimedVerifiedStatus
1HateBERTMacro F10.49—Unverified
2BERTMacro F10.48—Unverified
#ModelMetricClaimedVerifiedStatus
1HateBERTMacro F10.81—Unverified
2BERTMacro F10.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Multilingual BERTF1-score0.75—Unverified
2AutoMLF1-score0.74—Unverified
#ModelMetricClaimedVerifiedStatus
1AOM mBERTF10.85—Unverified
#ModelMetricClaimedVerifiedStatus
1BaselineF10.7—Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-large-STMacro F180.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Baseline BERT (task A)F10.77—Unverified