visolex/VN-HSD

Dataset Card for ViSoLex‑HSD

1. Dataset Summary

ViSoLex‑HSD is a unified Vietnamese hate‐speech detection corpus, combining three benchmark datasets:

• ViHSD (Son et al., 2021): 33K comments labeled CLEAN, OFFENSIVE, or HATE
• UIT‑ViCTSD (Nguyen et al., 2020): 10K comments annotated for TOXIC (mapped to HATE) or CLEAN
• ViHOS (Hoang et al., 2023): span‐level labels aggregated into comment‐level HATE/CLEAN

After renaming and mapping labels (0 = CLEAN; 1 = OFFENSIVE; 2 = HATE) and concatenating - with duplicate comments removed - the final DataFrame contains:

• Columns:

  • dataset: original source (ViHSD/ViCTSD/ViHOS)
  • type: split indicator (train/validation/test)
  • comment: raw text
  • label: numeric (0/1/2)

2. Supported Tasks and Metrics

• Task: Text classification – Hate speech detection

• Labels:

  • 0 → CLEAN (no offensive content)
  • 1 → OFFENSIVE (non‐hate offensive language)
  • 2 → HATE (hate speech)

• Metrics: Accuracy, Precision/Recall/F1 per class

3. Languages

• Vietnamese

4. Dataset Structure

Column	Type	Description
dataset	string	Origin: ViHSD / ViCTSD / ViHOS
type	string	Split: train / validation / test
comment	string	The social‐media comment in Vietnamese
label	int	0=CLEAN, 1=OFFENSIVE, 2=HATE

6. Usage

from datasets import load_dataset

ds = load_dataset("your-namespace/visolex-hsd")

train_ds = ds.filter(lambda x: x["type"] == "train")
val_ds   = ds.filter(lambda x: x["type"] == "dev")
test_ds  = ds.filter(lambda x: x["type"] == "test")

print(train_ds.features)
print(train_ds[0])

7. Dataset Creation & Processing

1. Load original CSVs for ViHSD, ViCTSD, ViHOS.

2. Rename columns to comment and label.

3. Map labels:

   • ViHSD: keep 0/1/2.
   • ViCTSD: map Toxicity 1→2, 0→0.
   • ViHOS: span‐exists→2, no span→0.

4. Concatenate, retain only dataset, type, comment, label.

5. Drop duplicates on comment.

(Refer to the code snippet in the prompt.)

8. Source & Links

• ViHSD:

  • GitHub: https://github.com/sonlam1102/vihsd
  • Hugging Face: https://huggingface.co/datasets/sonlam1102/vihsd
  • Paper: Luu et al. (2021), “A large-scale dataset for hate speech detection on Vietnamese social media texts.”

• UIT‑ViCTSD:

  • GitHub: https://github.com/tarudesu/ViCTSD
  • Hugging Face: https://huggingface.co/datasets/tarudesu/ViCTSD
  • Paper: “Constructive and Toxic Speech Detection for Open-domain Social Media Comments in Vietnamese” (Nguyen et al., 2020).

• ViHOS:

  • GitHub: https://github.com/phusroyal/ViHOS
  • Hugging Face: https://huggingface.co/datasets/phusroyal/ViHOS
  • Paper: Hoang et al. (2023), “ViHOS: Hate Speech Spans Detection for Vietnamese.”

9. Licenses & Citation

Please see each source’s license. If unspecified, assume MIT or CC BY 4.0.

Citation Information:

@inproceedings{luu2021large,
  title={A large-scale dataset for hate speech detection on vietnamese social media texts},
  author={Luu, Son T and Nguyen, Kiet Van and Nguyen, Ngan Luu-Thuy},
  booktitle={Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices: 34th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2021, Kuala Lumpur, Malaysia, July 26--29, 2021, Proceedings, Part I 34},
  pages={415--426},
  year={2021},
  organization={Springer}
}

@InProceedings{nguyen2021victsd,
author="Nguyen, Luan Thanh and Van Nguyen, Kiet and Nguyen, Ngan Luu-Thuy",
title="Constructive and Toxic Speech Detection for Open-Domain Social Media Comments in Vietnamese",
booktitle="Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices",
year="2021",
publisher="Springer International Publishing",
address="Cham",
pages="572--583"
}

@inproceedings{hoang-etal-2023-vihos,
    title = "{V}i{HOS}: Hate Speech Spans Detection for {V}ietnamese",
    author = "Hoang, Phu Gia  and
      Luu, Canh Duc  and
      Tran, Khanh Quoc  and
      Nguyen, Kiet Van  and
      Nguyen, Ngan Luu-Thuy",
    booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.eacl-main.47",
    doi = "10.18653/v1/2023.eacl-main.47",
    pages = "652--669"
    }