---
license: mit
---

# BanTH: A Multi-label Hate Speech Detection Dataset for Transliterated Bangla

[![code](https://img.shields.io/badge/Code-farhanishmam/BanTH-blue?logo=GitHub)](https://github.com/farhanishmam/BanTH)

## 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.       |