Hate Speech Detection Using Cross-Platform Social Media Data In English and German Language
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| Format: | Preprint |
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2024
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| _version_ | 1866914966962962432 |
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| author | Shahi, Gautam Kishore Majchrzak, Tim A. |
| author_facet | Shahi, Gautam Kishore Majchrzak, Tim A. |
| contents | Hate speech has grown into a pervasive phenomenon, intensifying during times of crisis, elections, and social unrest. Multiple approaches have been developed to detect hate speech using artificial intelligence, but a generalized model is yet unaccomplished. The challenge for hate speech detection as text classification is the cost of obtaining high-quality training data. This study focuses on detecting bilingual hate speech in YouTube comments and measuring the impact of using additional data from other platforms in the performance of the classification model. We examine the value of additional training datasets from cross-platforms for improving the performance of classification models. We also included factors such as content similarity, definition similarity, and common hate words to measure the impact of datasets on performance. Our findings show that adding more similar datasets based on content similarity, hate words, and definitions improves the performance of classification models. The best performance was obtained by combining datasets from YouTube comments, Twitter, and Gab with an F1-score of 0.74 and 0.68 for English and German YouTube comments. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_05287 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hate Speech Detection Using Cross-Platform Social Media Data In English and German Language Shahi, Gautam Kishore Majchrzak, Tim A. Computation and Language Artificial Intelligence Social and Information Networks Hate speech has grown into a pervasive phenomenon, intensifying during times of crisis, elections, and social unrest. Multiple approaches have been developed to detect hate speech using artificial intelligence, but a generalized model is yet unaccomplished. The challenge for hate speech detection as text classification is the cost of obtaining high-quality training data. This study focuses on detecting bilingual hate speech in YouTube comments and measuring the impact of using additional data from other platforms in the performance of the classification model. We examine the value of additional training datasets from cross-platforms for improving the performance of classification models. We also included factors such as content similarity, definition similarity, and common hate words to measure the impact of datasets on performance. Our findings show that adding more similar datasets based on content similarity, hate words, and definitions improves the performance of classification models. The best performance was obtained by combining datasets from YouTube comments, Twitter, and Gab with an F1-score of 0.74 and 0.68 for English and German YouTube comments. |
| title | Hate Speech Detection Using Cross-Platform Social Media Data In English and German Language |
| topic | Computation and Language Artificial Intelligence Social and Information Networks |
| url | https://arxiv.org/abs/2410.05287 |