Hate Speech Detection
169 papers with code • 14 benchmarks • 40 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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Hate Speech Detection models and implementationsDatasets
Hate Speech
OLID
ToxiGen
Implicit Hate
Subtasks
Latest papers with no code
An Investigation of Large Language Models for Real-World Hate Speech Detection
Our study reveals that a meticulously crafted reasoning prompt can effectively capture the context of hate speech by fully utilizing the knowledge base in LLMs, significantly outperforming existing techniques.
HCDIR: End-to-end Hate Context Detection, and Intensity Reduction model for online comments
We masked the 50\% hateful words of the comments identified as hateful and predicted the alternative words for these masked terms to generate convincing sentences.
Hate Speech and Offensive Content Detection in Indo-Aryan Languages: A Battle of LSTM and Transformers
Our study encompasses a wide range of pre-trained models, including Bert variants, XLM-R, and LSTM models, to assess their performance in identifying hate speech across these languages.
Contextualizing Internet Memes Across Social Media Platforms
Internet memes have emerged as a novel format for communication and expressing ideas on the web.
Generative AI for Hate Speech Detection: Evaluation and Findings
In addition, we explore and compare the performance of the finetuned LLMs with zero-shot hate detection using a GPT-3. 5 model.
LLMs and Finetuning: Benchmarking cross-domain performance for hate speech detection
To answer (2), we assessed the performance of 288 out-of-domain classifiers for a given end-domain dataset.
Break it, Imitate it, Fix it: Robustness by Generating Human-Like Attacks
We demonstrate the advantages of this system on the ANLI and hate speech detection benchmark datasets - both collected via an iterative, adversarial human-and-model-in-the-loop procedure.
GASCOM: Graph-based Attentive Semantic Context Modeling for Online Conversation Understanding
Specifically, we design two novel algorithms that utilise both the graph structure of the online conversation as well as the semantic information from individual posts for retrieving relevant context nodes from the whole conversation.
Probing LLMs for hate speech detection: strengths and vulnerabilities
Recently efforts have been made by social media platforms as well as researchers to detect hateful or toxic language using large language models.
Hate Speech Detection in Limited Data Contexts using Synthetic Data Generation
In this work, we propose a data augmentation approach that addresses the problem of lack of data for online hate speech detection in limited data contexts using synthetic data generation techniques.
