# Detecting Spam Reviews on Vietnamese E-commerce Websites

arXiv: https://arxiv.org/abs/2207.14636 · HTML: https://mlsanity.com/paper/2207.14636/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 27 Jul 2022.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [A Primer in BERTology: What we know about how BERT works](https://mlsanity.com/paper/2002.12327/) (2020)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [PhoBERT: Pre-trained language models for Vietnamese](https://mlsanity.com/paper/2003.00744/) (2020)
- [SA2SL: From Aspect-Based Sentiment Analysis to Social Listening System for Business Intelligence](https://mlsanity.com/paper/2105.15079/) (2021)
- [A Simple and Efficient Ensemble Classifier Combining Multiple Neural Network Models on Social Media Datasets in Vietnamese](https://mlsanity.com/paper/2009.13060/) (2020)
- [NLPBK at VLSP-2020 shared task: Compose transformer pretrained models for Reliable Intelligence Identification on Social network](https://mlsanity.com/paper/2101.12672/) (2021)
- [Investigating Monolingual and Multilingual BERTModels for Vietnamese Aspect Category Detection](https://mlsanity.com/paper/2103.09519/) (2021)
- [Span Detection for Aspect-Based Sentiment Analysis in Vietnamese](https://mlsanity.com/paper/2110.07833/) (2021)

## Built on by (most cited first, 4 of 4)

- [ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing](https://mlsanity.com/paper/2310.11166/) (2023)
- [Metadata Integration for Spam Reviews Detection on Vietnamese E-commerce Websites](https://mlsanity.com/paper/2405.13292/) (2024)
- [A Weakly Supervised Data Labeling Framework for Machine Lexical Normalization in Vietnamese Social Media](https://mlsanity.com/paper/2409.20467/) (2024)
- [Types of Approaches, Applications and Challenges in the Development of Sentiment Analysis Systems](https://mlsanity.com/paper/2303.11176/) (2023)

## Abstract (arXiv)

The reviews of customers play an essential role in online shopping. People often refer to reviews or comments of previous customers to decide whether to buy a new product. Catching up with this behavior, some people create untruths and illegitimate reviews to hoax customers about the fake quality of products. These are called spam reviews, confusing consumers on online shopping platforms and negatively affecting online shopping behaviors. We propose the dataset called ViSpamReviews, which has a strict annotation procedure for detecting spam reviews on e-commerce platforms. Our dataset consists of two tasks: the binary classification task for detecting whether a review is spam or not and the multi-class classification task for identifying the type of spam. The PhoBERT obtained the highest results on both tasks, 86.89% and 72.17%, respectively, by macro average F1 score.