README.md

metadata

task_categories:
  - text-classification
language:
  - vi

Dataset Card for ViSpamReviews

1. Dataset Summary

ViSpamReviews is a Vietnamese e‑commerce review dataset for spam detection, with both:

• Binary task: Label ∈ {0 = non‑spam, 1 = spam}.
• Multi‑class task: SpamLabel ∈ {0 = NO-SPAM, 1 = SPAM-1 (fake review), 2 = SPAM-2 (brand‑only), 3 = SPAM-3 (irrelevant)}.

It collects reviews from major Vietnamese online shopping platforms, annotated via a strict procedure to identify deceptive or irrelevant content.

2. Supported Tasks and Metrics

• Tasks

  • Binary classification: Is the review spam?
  • Multi‑class classification: Type of spam review.

• Metrics

  • Binary: Accuracy, macro F1
  • Multi‑class: Accuracy, macro F1

On PhoBERT, the original achieves 86.89% macro F1 on binary and 72.17% macro F1 on multi‑class.

3. Languages

• Vietnamese

4. Dataset Structure

The unified CSV has these columns:

Column	Type	Description
dataset	string	Source identifier (always ViSpamReviews in this unified version).
type	string	Split: train / validation / test.
comment	string	The raw user review text.
Label	int	Binary spam flag: 0 = non‑spam, 1 = spam.
SpamLabel	int	Multi‑class label: 0=NO-SPAM, 1=SPAM-1, 2=SPAM-2, 3=SPAM-3.

5. Data Fields

• Comment (str): The user's product review.
• Label (int): Binary spam label.
• SpamLabel (int): Spam type (0–3).
• type (str): Which split this example belongs to.
• dataset (str): Always ViSpamReviews for provenance.

6. Usage

from datasets import load_dataset

ds = load_dataset("visolex/vispamreviews")

train = ds.filter(lambda ex: ex["type"] == "train")
val   = ds.filter(lambda ex: ex["type"] == "dev")
test  = ds.filter(lambda ex: ex["type"] == "test")

print(train[0])

7. Source & Links

• GitHub (original code & raw data) https://github.com/sonlam1102/vispamdetection
• Original Paper Van Dinh et al. (2022), “Detecting Spam Reviews on Vietnamese E‑Commerce Websites” ([arxiv.org][1])

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8. Licensing and Citation

License

Refer to the GitHub repo’s LICENSE. If unspecified, assume CC BY 4.0.

How to Cite

@InProceedings{10.1007/978-3-031-21743-2_48,
  author    = {Van Dinh, Co and Luu, Son T. and Nguyen, Anh Gia-Tuan},
  title     = {Detecting Spam Reviews on Vietnamese E-Commerce Websites},
  booktitle = {Intelligent Information and Database Systems},
  year      = {2022},
  publisher = {Springer International Publishing},
  pages     = {595--607},
  isbn      = {978-3-031-21743-2}
}

@misc{sonlam1102_vispamdetection,
  title        = {ViSpamReviews: Spam Reviews Detection on Vietnamese E‑Commerce},
  author       = {{sonlam1102}},
  howpublished = {\url{https://github.com/sonlam1102/vispamdetection}},
  year         = {2022}
}