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license: mit |
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# BanTH: A Multi-label Hate Speech Detection Dataset for Transliterated Bangla |
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[](https://github.com/farhanishmam/BanTH) |
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## Dataset Overview |
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- Hate detection (binary) and target identification (multi-label) dataset on 37.3 k transliterated Bangla samples. |
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- ~38% hate samples and 7 target classes: Political, Religious, Gender, Personal Offense, Abusive/Violence, Origin, and Body Shaming. |
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- Sourced from 26 YouTube channels of 3 categories: News & Politics, People & Blogs, and Entertainment. |
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- Each sample is labeled by 3 annotators and verified by experts. |
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## Data Format |
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| **Column Title** | **Description** | |
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|----------------------------|------------------------------------------------| |
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| `text_id` | Unique identifier for each text entry. | |
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| `text_content` | The actual transliterated Bangla text. | |
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| `hate label` | Indicates whether the text contains hate speech (Yes/No). | |
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| `political` | Flags political hate content (Yes/No). | |
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| `religious` | Flags religious hate content (Yes/No). | |
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| `gender` | Identifies gender-based hate content (Yes/No). | |
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| `personal offense` | Indicates if the text contains personal attacks (Yes/No). | |
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| `abusive` | Flags text with abusive language or can promote violence (Yes/No). | |
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| `origin` | Hate speech targeting someone's origin, e.g. race, nationality (Yes/No). | |
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| `body_shaming` | Identifies body-shaming content (Yes/No). | |
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| `BN` | Back-transliteration of the text content in Bangla. | |
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| `EN` | Translation of the text content in English. | |
