---
dataset_info:
  features:
  - name: cleaned_text
    dtype: string
  - name: label
    dtype: string
  - name: __index_level_0__
    dtype: int64
  splits:
  - name: train
    num_bytes: 3822142
    num_examples: 30240
  - name: validation
    num_bytes: 479893
    num_examples: 3780
  - name: test
    num_bytes: 474875
    num_examples: 3780
  download_size: 3126764
  dataset_size: 4776910
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
license: cc
task_categories:
- text-classification
language:
- en
tags:
- cyberbullying
- nlp
---


# Cyberbullying Dataset

## Overview
This dataset combines five public datasets (tdavidson, OLID, Stormfront, Gab Hate Corpus, and HateXplain) to create a comprehensive resource for training and evaluating binary text classification models to detect cyberbullying. It contains ~30,000 balanced text samples labeled as "bully" (hate speech, offensive) or "normal" (non-offensive), sourced from Twitter, Gab, and Stormfront forums.

## Dataset Structure
- **Splits**: 
  - Train: ~30k samples (~80%)
  - Validation: ~4k samples (~10%)
  - Test: ~4k samples (~10%)
- **Columns**:
  - `cleaned_text`: Preprocessed text (lowercase, mentions/URLs/newlines removed, basic punctuation kept, numbers/emojis dropped, max 50 words).
  - `label`: Binary label ("bully" or "normal").
- **Class Balance**: Equal number of "bully" and "normal" samples in each split.

## Preprocessing
- Combined from tdavidson, OLID, Stormfront, Gab Hate Corpus, and HateXplain.
- Unified labels: "hate"/"offensive" mapped to "bully", "no_hate"/"normal" to "normal".
- Applied consistent cleaning: removed mentions, URLs, newlines; converted to lowercase; kept basic punctuation; capped at 50 words.
- Deduplicated and balanced classes to ensure robustness.

## Usage
Ideal for fine-tuning LLMs for binary text classification (e.g., detecting cyberbullying). Example prompt format:
```
Classify this text: {cleaned_text}
Response: {label}
```

Load with Hugging Face `datasets`:
```python
from datasets import load_dataset
dataset = load_dataset("cike-dev/cyberbullying_dataset")
```

## Sources and Citations
This dataset aggregates the following sources:
- tdavidson: Davidson, T., Warmsley, D., Macy, M., & Weber, I. (2017). Automated hate speech detection and the problem of offensive language. In Proceedings of the 11th International AAAI Conference on Web and Social Media (ICWSM ’17) (pp. 512–515). Montreal, Canada.
- OLID: Zampieri, M., Malmasi, S., Nakov, P., Rosenthal, S., Farra, N., & Kumar, R. (2019). Predicting the type and target of offensive posts in social media. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL).
- Stormfront: de Gibert, O., Perez, N., García-Pablos, A., & Cuadros, M. (2018, October). Hate speech dataset from a white supremacy forum. In Proceedings of the 2nd Workshop on Abusive Language Online (ALW2) (pp. 11–20). Association for Computational Linguistics. https://doi.org/10.18653/v1/W18-5102
- Gab Hate Corpus: Kennedy, B., Atari, M., Davani, A. M., Yeh, L., Omrani, A., Kim, Y., Coombs, K., Portillo-Wightman, G., Havaldar, S., Gonzalez, E., et al. (2022, April). The Gab Hate Corpus. OSF. https://doi.org/10.17605/OSF.IO/EDUA3
- HateXplain: Mathew, B., Saha, P., Yimam, S. M., Biemann, C., Goyal, P., & Mukherjee, A. (2021). HateXplain: A benchmark dataset for explainable hate speech detection. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 14867–14875.

## License
The dataset is released under CC-BY 4.0, respecting the licenses of the original datasets. Please cite the sources above when using this dataset.

## Contact
For issues or questions, open an issue on the Hugging Face repository or contact the maintainer.