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 with no code
Z-AGI Labs at ClimateActivism 2024: Stance and Hate Event Detection on Social Media
Addressing the growing need for high-quality information on events and the imperative to combat hate speech, this research led to the establishment of the Shared Task on Climate Activism Stance and Hate Event Detection at CASE 2024.
MM-Soc: Benchmarking Multimodal Large Language Models in Social Media Platforms
Social media platforms are hubs for multimodal information exchange, encompassing text, images, and videos, making it challenging for machines to comprehend the information or emotions associated with interactions in online spaces.
Don't Go To Extremes: Revealing the Excessive Sensitivity and Calibration Limitations of LLMs in Implicit Hate Speech Detection
This serves as a reminder to carefully consider sensitivity and confidence in the pursuit of model fairness.
Whose Emotions and Moral Sentiments Do Language Models Reflect?
We define the problem of affective alignment, which measures how LMs' emotional and moral tone represents those of different groups.
Personalized Large Language Models
Large language models (LLMs) have significantly advanced Natural Language Processing (NLP) tasks in recent years.
Exploring the Adversarial Capabilities of Large Language Models
The proliferation of large language models (LLMs) has sparked widespread and general interest due to their strong language generation capabilities, offering great potential for both industry and research.
Identifying False Content and Hate Speech in Sinhala YouTube Videos by Analyzing the Audio
YouTube faces a global crisis with the dissemination of false information and hate speech.
Multilingual acoustic word embeddings for zero-resource languages
This research addresses the challenge of developing speech applications for zero-resource languages that lack labelled data.
Analysis and Detection of Multilingual Hate Speech Using Transformer Based Deep Learning
In this work, the proposed method is using transformer-based model to detect hate speech in social media, like twitter, Facebook, WhatsApp, Instagram, etc.
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.
