Hate Speech Detection Using Large Language Models: A Comprehensive Review
Auburn University · University of North Alabama · +2 more institutions
Abstract
The widespread use of social media and other online platforms has facilitated unprecedented communication and information exchange. However, it has also led to the spread of hate speech and poses serious challenges to societal harmony as well as individual well-being. Traditional methods for detecting hate speech, such as keyword matching, rule-based systems, and machine learning algorithms, often struggle to capture the subtle and context-dependent nature of hateful content. This paper provides a comprehensive review of the application of large language models (LLMs) like GPT-3, BERT, and their successors in hate speech detection. We analyze the evolution of LLMs in natural language processing and examine…
Citation impact
- FWCI
- 85.98
- Percentile
- 100%
- References
- 131
Authors
10Topics & keywords
- Computer science
- Speech recognition
- Natural language processing
- Artificial intelligence
- Reduced inequalities