A multilingual dataset for offensive language and hate speech detection for hausa, yoruba and igbo languages
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866917686147022848 |
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| author | Aliyu, Saminu Mohammad Wajiga, Gregory Maksha Murtala, Muhammad |
| author_facet | Aliyu, Saminu Mohammad Wajiga, Gregory Maksha Murtala, Muhammad |
| contents | The proliferation of online offensive language necessitates the development of effective detection mechanisms, especially in multilingual contexts. This study addresses the challenge by developing and introducing novel datasets for offensive language detection in three major Nigerian languages: Hausa, Yoruba, and Igbo. We collected data from Twitter and manually annotated it to create datasets for each of the three languages, using native speakers. We used pre-trained language models to evaluate their efficacy in detecting offensive language in our datasets. The best-performing model achieved an accuracy of 90\%. To further support research in offensive language detection, we plan to make the dataset and our models publicly available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_02169 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A multilingual dataset for offensive language and hate speech detection for hausa, yoruba and igbo languages Aliyu, Saminu Mohammad Wajiga, Gregory Maksha Murtala, Muhammad Computation and Language 14F05 F.2.2; I.2.7 The proliferation of online offensive language necessitates the development of effective detection mechanisms, especially in multilingual contexts. This study addresses the challenge by developing and introducing novel datasets for offensive language detection in three major Nigerian languages: Hausa, Yoruba, and Igbo. We collected data from Twitter and manually annotated it to create datasets for each of the three languages, using native speakers. We used pre-trained language models to evaluate their efficacy in detecting offensive language in our datasets. The best-performing model achieved an accuracy of 90\%. To further support research in offensive language detection, we plan to make the dataset and our models publicly available. |
| title | A multilingual dataset for offensive language and hate speech detection for hausa, yoruba and igbo languages |
| topic | Computation and Language 14F05 F.2.2; I.2.7 |
| url | https://arxiv.org/abs/2406.02169 |