Hate Speech Detection
164 papers with code • 14 benchmarks • 39 datasets
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
These leaderboards are used to track progress in Hate Speech Detection
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Libraries
Use these libraries to find Hate Speech Detection models and implementationsDatasets
Hate Speech
OLID
ToxiGen
Implicit Hate
Subtasks
Latest papers
TurkishBERTweet: Fast and Reliable Large Language Model for Social Media Analysis
Wide us of this language on social media platforms such as Twitter, Instagram, or Tiktok and strategic position of the country in the world politics makes it appealing for the social network researchers and industry.
Improving Cross-Domain Hate Speech Generalizability with Emotion Knowledge
sy-hong/ek-hs-generalizability
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Reliable automatic hate speech (HS) detection systems must adapt to the in-flow of diverse new data to curtail hate speech.
Latent Feature-based Data Splits to Improve Generalisation Evaluation: A Hate Speech Detection Case Study
maikezuefle/latent-feature-splits
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We challenge hate speech models via new train-test splits of existing datasets that rely on the clustering of models' hidden representations.
GPT-4V(ision) as A Social Media Analysis Engine
Our investigation begins with a preliminary quantitative analysis for each task using existing benchmark datasets, followed by a careful review of the results and a selection of qualitative samples that illustrate GPT-4V's potential in understanding multimodal social media content.
Automatic Textual Normalization for Hate Speech Detection
Our dataset is accessible for research purposes.
mahaNLP: A Marathi Natural Language Processing Library
We present mahaNLP, an open-source natural language processing (NLP) library specifically built for the Marathi language.
HARE: Explainable Hate Speech Detection with Step-by-Step Reasoning
joonkeekim/hare-hate-speech
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With the proliferation of social media, accurate detection of hate speech has become critical to ensure safety online.
K-HATERS: A Hate Speech Detection Corpus in Korean with Target-Specific Ratings
This resource is the largest offensive language corpus in Korean and is the first to offer target-specific ratings on a three-point Likert scale, enabling the detection of hate expressions in Korean across varying degrees of offensiveness.
InterroLang: Exploring NLP Models and Datasets through Dialogue-based Explanations
While recently developed NLP explainability methods let us open the black box in various ways (Madsen et al., 2022), a missing ingredient in this endeavor is an interactive tool offering a conversational interface.
KoMultiText: Large-Scale Korean Text Dataset for Classifying Biased Speech in Real-World Online Services
With the growth of online services, the need for advanced text classification algorithms, such as sentiment analysis and biased text detection, has become increasingly evident.
