Hate Speech Detection
159 papers with code • 14 benchmarks • 38 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.
Libraries
Use these libraries to find Hate Speech Detection models and implementationsDatasets
Subtasks
Latest papers with no code
Towards Interpretable Hate Speech Detection using Large Language Model-extracted Rationales
Although social media platforms are a prominent arena for users to engage in interpersonal discussions and express opinions, the facade and anonymity offered by social media may allow users to spew hate speech and offensive content.
Exploring Tokenization Strategies and Vocabulary Sizes for Enhanced Arabic Language Models
This paper presents a comprehensive examination of the impact of tokenization strategies and vocabulary sizes on the performance of Arabic language models in downstream natural language processing tasks.
Harnessing Artificial Intelligence to Combat Online Hate: Exploring the Challenges and Opportunities of Large Language Models in Hate Speech Detection
Large language models (LLMs) excel in many diverse applications beyond language generation, e. g., translation, summarization, and sentiment analysis.
Subjective $\textit{Isms}$? On the Danger of Conflating Hate and Offence in Abusive Language Detection
Natural language processing research has begun to embrace the notion of annotator subjectivity, motivated by variations in labelling.
Leveraging Weakly Annotated Data for Hate Speech Detection in Code-Mixed Hinglish: A Feasibility-Driven Transfer Learning Approach with Large Language Models
Zero-shot learning, one-shot learning, and few-shot learning and prompting approaches have then been applied to assign labels to the comments and compare them to human-assigned labels.
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.