Omkar2118/datasets huggingface dataset:
OffensiveLang

OffensiveLang is a community based implicit offensive language dataset generated by ChatGPT 3.5 containing data for 38 different target groups. It has been meticulously annotated by Amazon MTurk workers, ensuring high-quality labeling of hate speech. Additionally, a prompt-based zero-shot method was employed with ChatGPT and the detection results were compared between human annotation and ChatGPT annotation. This dataset is invaluable for researchers and practitioners working on implicit hate speech detection and large language models.

**Source:** ChatGPT 3.5  
**Length:** 8270 texts
*train:* 6616 texts
*test:* 1654 texts

##OffensiveLang.csv_Details

Column1: Text: Contains text generated by ChatGPT 3.5.
Column2: Category: Represents the category of the target group.
Column3: Target Group: Specifies the target group for the text.
Column4: Final Annotation: The final human annotation determined by the majority vote among three MTurk annotators (this annotation will be used as the actual annotation for model training and evaluation).
Column5: OpenAI_Annotation: Annotation provided by ChatGPT 3.5.
Column6-8: Annotator1-3: Individual annotations from three human annotators.

## Paper
For a detailed investigation of the OffensiveLang dataset, refer to the associated paper: [OffensiveLang: A Community Based Implicit Offensive Language Dataset](https://arxiv.org/abs/2403.02472).

## Citation
If you use this dataset in your research, please cite the following paper:

@article{das2024offlandat,
  title={OffLanDat: A Community Based Implicit Offensive Language Dataset Generated by Large Language Model Through Prompt Engineering},
  author={Das, Amit and Rahgouy, Mostafa and Feng, Dongji and Zhang, Zheng and Bhattacharya, Tathagata and Raychawdhary, Nilanjana and Sandage, Mary and Pope, Lauramarie and Dozier, Gerry and Seals, Cheryl},
  journal={arXiv preprint arXiv:2403.02472},
  year={2024}
}

## License: CC BY

## Contact
For any questions or feedback, feel free to contact the authors of the paper or the dataset contributors.
Contact Information: azd0123@auburn.edu
---

Introducing paper: OffensiveLang: A Community Based Implicit Offensive Language Dataset
HateSpeechCorpus

HateSpeechCorpus is a dataset collected from Twitter, consisting of 3003 tweets. It has been meticulously annotated by three speech-language pathology graduate students, ensuring high-quality labeling of hate speech. This dataset is invaluable for researchers and practitioners working on hate speech detection and natural language processing.

**Source:** Twitter  
**Length:** 3003 tweets  
**Annotators:** Three speech-language pathology graduate students  

## Paper
For a detailed investigation of annotator bias in LLMs for hate speech detection, refer to the associated paper: [Investigating Annotator Bias in Large Language Models for Hate Speech Detection](https://arxiv.org/abs/2406.11109).

## Citation
If you use this dataset in your research, please cite the following paper:

@misc{das2024investigating,
title={Investigating Annotator Bias in Large Language Models for Hate Speech Detection},
author={Amit Das and Zheng Zhang and Fatemeh Jamshidi and Vinija Jain and Aman Chadha and Nilanjana Raychawdhary and Mary Sandage and Lauramarie Pope and Gerry Dozier and Cheryl Seals},
year={2024},
eprint={2406.11109},
archivePrefix={arXiv},
primaryClass={cs.CL}
}

# Load the dataset
df = pd.read_csv('HateSpeechCorpus.csv')

# Display the first few rows
print(df.head())

## License: CC BY

Contact Information: azd0123@auburn.edu
---

Introducing paper: Investigating Annotator Bias in Large Language Models for Hate Speech Detection