Hate Speech Detection Using Large Language Models: A Comprehensive Review
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 detecti...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10848067/ |
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| _version_ | 1832540554802495488 |
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| author | Aish Albladi Minarul Islam Amit Das Maryam Bigonah Zheng Zhang Fatemeh Jamshidi Mostafa Rahgouy Nilanjana Raychawdhary Daniela Marghitu Cheryl Seals |
| author_facet | Aish Albladi Minarul Islam Amit Das Maryam Bigonah Zheng Zhang Fatemeh Jamshidi Mostafa Rahgouy Nilanjana Raychawdhary Daniela Marghitu Cheryl Seals |
| author_sort | Aish Albladi |
| collection | DOAJ |
| description | 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 their strengths and limitations in identifying hate speech. Additionally, we address the significant challenges and explore how LLMs method can affect the accuracy and fairness of hate speech detection systems. By synthesizing recent research, this review aims to offer a holistic understanding of the current state-of-the-art methods in hate speech detection utilizing LLMs and to suggest directions for future research that could enhance the efficacy and equity of these systems. |
| format | Article |
| id | doaj-art-bb1e5c114f7845fba621e09c5cbaba93 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-bb1e5c114f7845fba621e09c5cbaba932025-02-05T00:01:06ZengIEEEIEEE Access2169-35362025-01-0113208712089210.1109/ACCESS.2025.353239710848067Hate Speech Detection Using Large Language Models: A Comprehensive ReviewAish Albladi0https://orcid.org/0009-0003-5738-4711Minarul Islam1https://orcid.org/0000-0003-0018-6194Amit Das2https://orcid.org/0000-0003-4190-3903Maryam Bigonah3https://orcid.org/0009-0004-9053-0634Zheng Zhang4https://orcid.org/0000-0003-1707-624XFatemeh Jamshidi5Mostafa Rahgouy6Nilanjana Raychawdhary7https://orcid.org/0009-0006-8479-1971Daniela Marghitu8https://orcid.org/0000-0002-7877-3623Cheryl Seals9Auburn University, Auburn, AL, USAAuburn University, Auburn, AL, USAUniversity of North Alabama, Florence, AL, USAAuburn University, Auburn, AL, USAMurray State University, Murray, KY, USACalifornia State Polytechnic University, Pomona, CA, USAAuburn University, Auburn, AL, USAAuburn University, Auburn, AL, USAAuburn University, Auburn, AL, USAAuburn University, Auburn, AL, USAThe 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 their strengths and limitations in identifying hate speech. Additionally, we address the significant challenges and explore how LLMs method can affect the accuracy and fairness of hate speech detection systems. By synthesizing recent research, this review aims to offer a holistic understanding of the current state-of-the-art methods in hate speech detection utilizing LLMs and to suggest directions for future research that could enhance the efficacy and equity of these systems.https://ieeexplore.ieee.org/document/10848067/Deep learninghate speech detectionlarge language modelsmachine learning |
| spellingShingle | Aish Albladi Minarul Islam Amit Das Maryam Bigonah Zheng Zhang Fatemeh Jamshidi Mostafa Rahgouy Nilanjana Raychawdhary Daniela Marghitu Cheryl Seals Hate Speech Detection Using Large Language Models: A Comprehensive Review IEEE Access Deep learning hate speech detection large language models machine learning |
| title | Hate Speech Detection Using Large Language Models: A Comprehensive Review |
| title_full | Hate Speech Detection Using Large Language Models: A Comprehensive Review |
| title_fullStr | Hate Speech Detection Using Large Language Models: A Comprehensive Review |
| title_full_unstemmed | Hate Speech Detection Using Large Language Models: A Comprehensive Review |
| title_short | Hate Speech Detection Using Large Language Models: A Comprehensive Review |
| title_sort | hate speech detection using large language models a comprehensive review |
| topic | Deep learning hate speech detection large language models machine learning |
| url | https://ieeexplore.ieee.org/document/10848067/ |
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