A Large Language Model-Based Approach for Multilingual Hate Speech Detection on Social Media
The proliferation of hate speech on social media platforms poses significant threats to digital safety, social cohesion, and freedom of expression. Detecting such content—especially across diverse languages—remains a challenging task due to linguistic complexity, cultural context, and resource limit...
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MDPI AG
2025-07-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/14/7/279 |
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| author | Muhammad Usman Muhammad Ahmad Grigori Sidorov Irina Gelbukh Rolando Quintero Tellez |
| author_facet | Muhammad Usman Muhammad Ahmad Grigori Sidorov Irina Gelbukh Rolando Quintero Tellez |
| author_sort | Muhammad Usman |
| collection | DOAJ |
| description | The proliferation of hate speech on social media platforms poses significant threats to digital safety, social cohesion, and freedom of expression. Detecting such content—especially across diverse languages—remains a challenging task due to linguistic complexity, cultural context, and resource limitations. To address these challenges, this study introduces a comprehensive approach for multilingual hate speech detection. To facilitate robust hate speech detection across diverse languages, this study makes several key contributions. First, we created a novel trilingual hate speech dataset consisting of 10,193 manually annotated tweets in English, Spanish, and Urdu. Second, we applied two innovative techniques—joint multilingual and translation-based approaches—for cross-lingual hate speech detection that have not been previously explored for these languages. Third, we developed detailed hate speech annotation guidelines tailored specifically to all three languages to ensure consistent and high-quality labeling. Finally, we conducted 41 experiments employing machine learning models with TF–IDF features, deep learning models utilizing FastText and GloVe embeddings, and transformer-based models leveraging advanced contextual embeddings to comprehensively evaluate our approach. Additionally, we employed a large language model with advanced contextual embeddings to identify the best solution for the hate speech detection task. The experimental results showed that our GPT-3.5-turbo model significantly outperforms strong baselines, achieving up to an 8% improvement over XLM-R in Urdu hate speech detection and an average gain of 4% across all three languages. This research not only contributes a high-quality multilingual dataset but also offers a scalable and inclusive framework for hate speech detection in underrepresented languages. |
| format | Article |
| id | doaj-art-09e208cdd660403a8e1a904d5e0c84c9 |
| institution | Kabale University |
| issn | 2073-431X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Computers |
| spelling | doaj-art-09e208cdd660403a8e1a904d5e0c84c92025-08-20T03:36:19ZengMDPI AGComputers2073-431X2025-07-0114727910.3390/computers14070279A Large Language Model-Based Approach for Multilingual Hate Speech Detection on Social MediaMuhammad Usman0Muhammad Ahmad1Grigori Sidorov2Irina Gelbukh3Rolando Quintero Tellez4Centro de Investigación en Computación, Instituto Politécnico Nacional (CIC-IPN), Mexico City 07320, MexicoCentro de Investigación en Computación, Instituto Politécnico Nacional (CIC-IPN), Mexico City 07320, MexicoCentro de Investigación en Computación, Instituto Politécnico Nacional (CIC-IPN), Mexico City 07320, MexicoCentro de Investigación en Computación, Instituto Politécnico Nacional (CIC-IPN), Mexico City 07320, MexicoCentro de Investigación en Computación, Instituto Politécnico Nacional (CIC-IPN), Mexico City 07320, MexicoThe proliferation of hate speech on social media platforms poses significant threats to digital safety, social cohesion, and freedom of expression. Detecting such content—especially across diverse languages—remains a challenging task due to linguistic complexity, cultural context, and resource limitations. To address these challenges, this study introduces a comprehensive approach for multilingual hate speech detection. To facilitate robust hate speech detection across diverse languages, this study makes several key contributions. First, we created a novel trilingual hate speech dataset consisting of 10,193 manually annotated tweets in English, Spanish, and Urdu. Second, we applied two innovative techniques—joint multilingual and translation-based approaches—for cross-lingual hate speech detection that have not been previously explored for these languages. Third, we developed detailed hate speech annotation guidelines tailored specifically to all three languages to ensure consistent and high-quality labeling. Finally, we conducted 41 experiments employing machine learning models with TF–IDF features, deep learning models utilizing FastText and GloVe embeddings, and transformer-based models leveraging advanced contextual embeddings to comprehensively evaluate our approach. Additionally, we employed a large language model with advanced contextual embeddings to identify the best solution for the hate speech detection task. The experimental results showed that our GPT-3.5-turbo model significantly outperforms strong baselines, achieving up to an 8% improvement over XLM-R in Urdu hate speech detection and an average gain of 4% across all three languages. This research not only contributes a high-quality multilingual dataset but also offers a scalable and inclusive framework for hate speech detection in underrepresented languages.https://www.mdpi.com/2073-431X/14/7/279large language modelsGPTmultilingual hate speech detectiondata miningmachine learningdeep learning |
| spellingShingle | Muhammad Usman Muhammad Ahmad Grigori Sidorov Irina Gelbukh Rolando Quintero Tellez A Large Language Model-Based Approach for Multilingual Hate Speech Detection on Social Media Computers large language models GPT multilingual hate speech detection data mining machine learning deep learning |
| title | A Large Language Model-Based Approach for Multilingual Hate Speech Detection on Social Media |
| title_full | A Large Language Model-Based Approach for Multilingual Hate Speech Detection on Social Media |
| title_fullStr | A Large Language Model-Based Approach for Multilingual Hate Speech Detection on Social Media |
| title_full_unstemmed | A Large Language Model-Based Approach for Multilingual Hate Speech Detection on Social Media |
| title_short | A Large Language Model-Based Approach for Multilingual Hate Speech Detection on Social Media |
| title_sort | large language model based approach for multilingual hate speech detection on social media |
| topic | large language models GPT multilingual hate speech detection data mining machine learning deep learning |
| url | https://www.mdpi.com/2073-431X/14/7/279 |
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