README.md

---
language:
- bn
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
tags:
- women-safety
- gender-bias
- social-media
- Bangla-NLP
- sentiment-analysis
pretty_name: >-
  WoNBias-Partial:  A Balanced Subset of the Original WoNBias Dataset for Review
---

# WoNBias-Partial:  A Balanced Subset of the Original WoNBias Dataset for Review 

## Dataset Details

### Overview
A manually annotated corpus of Bangla designed to benchmark and mitigate gender bias specifically targeting women. The dataset supports research in ethical NLP, hate speech detection, and computational social science.

## Dataset Description

### Basic Info
- **Purpose**: Detect gender bias against women in Bengali text
- **Language**: Bengali (Bangla)
- **Labels**:
  - `0`: Neutral (no bias)
  - `1`: Positive (supportive towards women in general)
  - `2`: Negative (contains bias against women)

### Data Sources
Collected from:
- Social media (TikTok, Facebook, Twitter, Instagram)
- Online forums
- News articles and comments
- Government reports

### Collection Methods
- Semi-automated tools with ethical safeguards:
  - Only public content collected
  - All personal info removed
  - Rate-limited scraping

### Partnerships
Developed with help from:
- Social media data
- Community reachout
- Local annotators 

### **Dataset Structure**
- **Format**: CSV
- **Columns**:
  - `Data`: Raw Bangla text.
  - `Label`: Integer (0, 1, or 2).

## Statistics
- **Total Samples**: 11,178  
- **Label Distribution**:  
  - `Neutral (0)`: 3,644 samples (32.6%)  
  - `Positive (1)`: 3,506 samples (31.4%)  
  - `Negative (2)`: 4,028 samples (36.0%)  

## Ethical Considerations

### Data Collection
- **Anonymization**:  
  - All user identifiers (usernames, URLs, locations) removed  
  - Metadata sanitized to prevent re-identification  
- **Consent**:  
  - For survey/interview data: Explicit participant consent obtained  
  - Public posts used under platform terms of service  

### Content Safeguards
- **Mental Health Protections**:  
  - Annotators provided with:  
    - Psychological support resources  
    - Regular breaks when handling toxic content   
- **Trigger Warnings**:  
  - Dataset documentation highlights potentially harmful content  

## Limitations

- **Scope Limitations**:
  - Focuses only on gender bias against women
  - Doesn't cover third gender or intersectional biases

- **Detection Challenges**:
  - Difficulty identifying implicit bias and contextual nuances
  - Performance varies across regional dialects

- **Language Coverage**:
  - Currently Bengali-only (may not generalize to other South Asian languages)

## Usage

### **How to Load**
```python
from datasets import load_dataset
dataset = load_dataset("won-bias/wonbias-partial-dataset")
```

## Citation
```bibtex
@dataset{wonbias_2024,
  title = {{Wonbias-Partial}: WoNBias-partial: A balanced subset of the original for review},
  author = {Nishat Tafannum, MD. Raisul Islam Aupi},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/won-bias/wonbias-partial-dataset},
  version = {1.0},
  license = {CC-BY-NC-SA-4.0},
}
```

## License
[![CC BY-NC-SA 4.0](https://img.shields.io/badge/License-CC_BY--NC--SA_4.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc-sa/4.0/)

This work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/).

### You are free to:
- **Share** — copy and redistribute the material in any medium or format
- **Adapt** — remix, transform, and build upon the material

### Under the following terms:
- **Attribution** — You must give appropriate credit, provide a link to the license, and indicate if changes were made.
- **NonCommercial** — You may not use the material for commercial purposes.
- **ShareAlike** — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.