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