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 101–150 of 507 papers

TitleStatusHype
Two Heads are Better than One: Nested PoE for Robust Defense Against Multi-BackdoorsCode0
NLP Systems That Can't Tell Use from Mention Censor Counterspeech, but Teaching the Distinction HelpsCode0
Securing Social Spaces: Harnessing Deep Learning to Eradicate Cyberbullying—0
A Comprehensive Study on NLP Data Augmentation for Hate Speech Detection: Legacy Methods, BERT, and LLMs—0
Improving Adversarial Data Collection by Supporting Annotators: Lessons from GAHD, a German Hate Speech DatasetCode0
NaijaHate: Evaluating Hate Speech Detection on Nigerian Twitter Using Representative DataCode0
Towards Interpretable Hate Speech Detection using Large Language Model-extracted RationalesCode0
Exploring Tokenization Strategies and Vocabulary Sizes for Enhanced Arabic Language ModelsCode0
Harnessing Artificial Intelligence to Combat Online Hate: Exploring the Challenges and Opportunities of Large Language Models in Hate Speech Detection—0
CLIcK: A Benchmark Dataset of Cultural and Linguistic Intelligence in KoreanCode1
Leveraging Weakly Annotated Data for Hate Speech Detection in Code-Mixed Hinglish: A Feasibility-Driven Transfer Learning Approach with Large Language Models—0
Subjective Isms? On the Danger of Conflating Hate and Offence in Abusive Language Detection—0
Z-AGI Labs at ClimateActivism 2024: Stance and Hate Event Detection on Social Media—0
GPT-HateCheck: Can LLMs Write Better Functional Tests for Hate Speech Detection?Code0
MM-Soc: Benchmarking Multimodal Large Language Models in Social Media PlatformsCode0
Don't Go To Extremes: Revealing the Excessive Sensitivity and Calibration Limitations of LLMs in Implicit Hate Speech Detection—0
Whose Emotions and Moral Sentiments Do Language Models Reflect?—0
Exploring the Adversarial Capabilities of Large Language Models—0
Personalized Large Language ModelsCode2
Bryndza at ClimateActivism 2024: Stance, Target and Hate Event Detection via Retrieval-Augmented GPT-4 and LLaMACode4
Probing Critical Learning Dynamics of PLMs for Hate Speech DetectionCode0
Identifying False Content and Hate Speech in Sinhala YouTube Videos by Analyzing the Audio—0
Analysis and Detection of Multilingual Hate Speech Using Transformer Based Deep Learning—0
Attentive Fusion: A Transformer-based Approach to Multimodal Hate Speech DetectionCode0
Multilingual acoustic word embeddings for zero-resource languages—0
MetaHate: A Dataset for Unifying Efforts on Hate Speech DetectionCode0
An Investigation of Large Language Models for Real-World Hate Speech Detection—0
TuPy-E: detecting hate speech in Brazilian Portuguese social media with a novel dataset and comprehensive analysis of modelsCode0
HCDIR: End-to-end Hate Context Detection, and Intensity Reduction model for online comments—0
Hate Speech and Offensive Content Detection in Indo-Aryan Languages: A Battle of LSTM and Transformers—0
TurkishBERTweet: Fast and Reliable Large Language Model for Social Media AnalysisCode1
Improving Cross-Domain Hate Speech Generalizability with Emotion KnowledgeCode0
Contextualizing Internet Memes Across Social Media Platforms—0
Latent Feature-based Data Splits to Improve Generalisation Evaluation: A Hate Speech Detection Case StudyCode0
Generative AI for Hate Speech Detection: Evaluation and Findings—0
GPT-4V(ision) as A Social Media Analysis EngineCode0
Automatic Textual Normalization for Hate Speech DetectionCode0
mahaNLP: A Marathi Natural Language Processing LibraryCode0
Explainable Identification of Hate Speech towards Islam using Graph Neural Networks—0
HARE: Explainable Hate Speech Detection with Step-by-Step ReasoningCode1
LLMs and Finetuning: Benchmarking cross-domain performance for hate speech detection—0
Break it, Imitate it, Fix it: Robustness by Generating Human-Like Attacks—0
K-HATERS: A Hate Speech Detection Corpus in Korean with Target-Specific RatingsCode1
GASCOM: Graph-based Attentive Semantic Context Modeling for Online Conversation Understanding—0
Probing LLMs for hate speech detection: strengths and vulnerabilities—0
InterroLang: Exploring NLP Models and Datasets through Dialogue-based ExplanationsCode0
KoMultiText: Large-Scale Korean Text Dataset for Classifying Biased Speech in Real-World Online ServicesCode1
Hate Speech Detection in Limited Data Contexts using Synthetic Data Generation—0
Harnessing Pre-Trained Sentence Transformers for Offensive Language Detection in Indian Languages—0
Cordyceps@LT-EDI: Patching Language-Specific Homophobia/Transphobia Classifiers with a Multilingual Understanding—0
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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 [LIME]AUROC0.85—Unverified
4BERT-HateXplain [Attn]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