---
configs:
- config_name: AbuseEval
  data_files:
  - split: explicit_train
    path: AbuseEval/explicit_train.jsonl
  - split: explicit_test
    path: AbuseEval/explicit_test.jsonl
  - split: implicit_train
    path: AbuseEval/implicit_train.jsonl
  - split: implicit_test
    path: AbuseEval/implicit_test.jsonl
- config_name: DynaHate
  data_files:
  - split: explicit_train
    path: DynaHate/explicit_train.jsonl
  - split: explicit_test
    path: DynaHate/explicit_test.jsonl
  - split: implicit_train
    path: DynaHate/implicit_train.jsonl
  - split: implicit_test
    path: DynaHate/implicit_test.jsonl
- config_name: Implicit-Hate-Corpus
  data_files:
  - split: explicit_train
    path: Implicit-Hate-Corpus/explicit_train.jsonl
  - split: explicit_test
    path: Implicit-Hate-Corpus/explicit_test.jsonl
  - split: implicit_train
    path: Implicit-Hate-Corpus/implicit_train.jsonl
  - split: implicit_test
    path: Implicit-Hate-Corpus/implicit_test.jsonl
- config_name: IsHate
  data_files:
  - split: explicit_train
    path: IsHate/explicit_train.jsonl
  - split: explicit_test
    path: IsHate/explicit_test.jsonl
  - split: implicit_train
    path: IsHate/implicit_train.jsonl
  - split: implicit_test
    path: IsHate/implicit_test.jsonl
task_categories:
- text-classification
language:
- en
tags:
- hate-speech
size_categories:
- 10K<n<100K
---

# CADET datasets

<div align="center">

[![GitHub](https://img.shields.io/badge/GitHub-cadet-181717.svg)](https://github.com/Shu-Wan/cadet)
[![Paper](https://img.shields.io/badge/Paper-arXiv-b31b1b.svg)](https://arxiv.org/abs/2510.07707)
[![Conference](https://img.shields.io/badge/WWW-2026-blue.svg)](https://www2026.thewebconf.org/accepted.html)

</div>

Datasets for paper "Causality Guided Representation Learning for Cross-Style Hate Speech Detection"

## Field Descriptions

Across datasets, the following fields are standardized:

- `text_id`: Unique identifier for each text sample (int64, sequential index)
- `text`: Input text content (string)
- `hate_label`: Binary hate label (0=non-hate, 1=hate)
- `avg`: Average Perspective API toxicity score (float, 0.0-1.0)
- `style`: Binary toxicity derived from `avg` (0=non-toxic, 1=toxic)
- `true_style`: Style from file naming (0=implicit, 1=explicit)
- `target`: Target demographic group (string)
- `target_conf`: Confidence score for target annotation (float, 0.0-1.0)

> [!NOTE] How `avg` Is Calculated
> We used the Perspective API to fetch toxicity scores for the following attributes,
> then averaged them per example:
>
> - TOXICITY, SEVERE_TOXICITY, IDENTITY_ATTACK, INSULT, PROFANITY, THREAT
>
> The resulting mean is stored in the `avg` column.

## Structure

- `<dataset_name>/<true_style>_<split>.jsonl`
- `<dataset_name>/meta_info.json`

## Meta info

Each dataset folder includes a meta_info.json with:

- dataset, created_at, git_commit
- available_styles, splits, counts per split
- features (standardized schema), label_names, and raw_sources
- Use dataset name as a configuration (subset).
- Use `<true_style>_<split>` as the splits for that configuration.

## Citation

If you use CADET in your research, please cite:

```bibtex
@article{zhao2025causality,
  title={Causality Guided Representation Learning for Cross-Style Hate Speech Detection},
  author={Zhao, Chengshuai and Wan, Shu and Sheth, Paras and Patwa, Karan and Candan, K Sel{\c{c}}uk and Liu, Huan},
  journal={arXiv preprint arXiv:2510.07707},
  year={2025}
}
```