metadata

annotations_creators:
  - crowdsourced
license: mit
multilinguality:
  - monolingual
source_datasets:
  - original
task_ids:
  - hate-speech-detection
task_categories:
  - text-classification
  - token-classification
language:
  - vi
pretty_name: ViHOS - Vietnamese Hate and Offensive Spans Dataset
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train_sequence_labeling_based_version
        path:
          - train_sl/syllable/train_BIO_syllable.csv
          - train_sl/syllable/dev_BIO_syllable.csv
          - train_sl/syllable/test_BIO_syllable.csv
          - train_sl/word/train_BIO_syllable.csv
          - train_sl/word/dev_BIO_syllable.csv
          - train_sl/word/test_BIO_syllable.csv
      - split: train_span_extraction_based_version
        path:
          - train_se/train.csv
          - train_se/dev.csv
      - split: test
        path: test/test.csv

Dataset Card for "ViHOS"

Dataset Description

Repository: ViHOS
Paper: EACL-ViHOS
Total amount of disk used: 2.6 MB

Dataset Motivation

The rise in hateful and offensive language directed at other users is one of the adverse side effects of the increased use of social networking platforms. This could make it difficult for human moderators to review tagged comments filtered by classification systems.

To help address this issue, we present the ViHOS (Vietnamese Hate and Offensive Spans) dataset, the first human-annotated corpus containing 26k spans on 11k online comments.

Our goal is to create a dataset that contains comprehensive hate and offensive thoughts, meanings, or opinions within the comments rather than just a lexicon of hate and offensive terms.

We also provide definitions of hateful and offensive spans in Vietnamese comments as well as detailed annotation guidelines. Futhermore, our solutions to deal with nine different online foul linguistic phenomena are also provided in the paper (e.g. Teencodes; Metaphors, metonymies; Hyponyms; Puns...).

We hope that this dataset will be useful for researchers and practitioners in the field of hate speech detection in general and hate spans detection in particular.

Dataset Summary

ViHOS contains 26,476 human-annotated spans on 11,056 comments (5,360 comments have hate and offensive spans, and 5,696 comments do not)

It is splitted into train, dev, and test set with following information:

Train set: 8,844 comments
Dev set: 1,106 comments
Test set: 1,106 comments

Citation Information

@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",
    abstract = "The rise in hateful and offensive language directed at other users is one of the adverse side effects of the increased use of social networking platforms. This could make it difficult for human moderators to review tagged comments filtered by classification systems. To help address this issue, we present the ViHOS (Vietnamese Hate and Offensive Spans) dataset, the first human-annotated corpus containing 26k spans on 11k comments. We also provide definitions of hateful and offensive spans in Vietnamese comments as well as detailed annotation guidelines. Besides, we conduct experiments with various state-of-the-art models. Specifically, XLM-R{\_}Large achieved the best F1-scores in Single span detection and All spans detection, while PhoBERT{\_}Large obtained the highest in Multiple spans detection. Finally, our error analysis demonstrates the difficulties in detecting specific types of spans in our data for future research. Our dataset is released on GitHub.",
}