Spam reviews are a pervasive problem on online platforms due to its
significant impact on reputation. However, research into spam detection in data
streams is scarce. Another concern lies in their need for transparency.
Consequently, this paper addresses those problems by proposing an online
solution for identifying and explaining spam reviews, incorporating data drift
adaptation. It integrates (i) incremental profiling, (ii) data drift detection
& adaptation, and (iii) identification of spam reviews employing Machine
Learning. The explainable mechanism displays a visual and textual prediction
explanation in a dashboard. The best results obtained reached up to 87 % spam
F-measure.

提出了一种在线解决方案，用于识别和解释垃圾评论，包括数据漂移适应，达到了 87% 的垃圾评论 F-measure。

在线检测及信息图解的垃圾评论与数据漂移适应

Online detection and infographic explanation of spam reviews with data  drift adaptation

The problem of detecting spam reviews (opinions) has received significant
attention in recent years, especially with the rapid development of e-commerce.
Spam reviews are often classified based on comment content, but in some cases,
it is insufficient for models to accurately determine the review label. In this
work, we introduce the ViSpamReviews v2 dataset, which includes metadata of
reviews with the objective of integrating supplementary attributes for spam
review classification. We propose a novel approach to simultaneously integrate
both textual and categorical attributes into the classification model. In our
experiments, the product category proved effective when combined with deep
neural network (DNN) models, while text features performed well on both DNN
models and the model achieved state-of-the-art performance in the problem of
detecting spam reviews on Vietnamese e-commerce websites, namely PhoBERT.
Specifically, the PhoBERT model achieves the highest accuracy when combined
with product description features generated from the SPhoBert model, which is
the combination of PhoBERT and SentenceBERT. Using the macro-averaged F1 score,
the task of classifying spam reviews achieved 87.22% (an increase of 1.64%
compared to the baseline), while the task of identifying the type of spam
reviews achieved an accuracy of 73.49% (an increase of 1.93% compared to the
baseline).

利用 PhoBERT 模型结合产品描述功能实现了检测越南电子商务网站上垃圾评论的最先进性能，可以通过同时整合文本和分类属性来提高垃圾评论分类的准确性。

越南电子商务网站垃圾评论检测的元数据整合

Metadata Integration for Spam Reviews Detection on Vietnamese E-commerce  Websites

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.

本文提出了一个叫做 ViSpamReviews 的数据集，用于检测电子商务平台上的垃圾评论，包含两个任务，一个是二分类任务，用于判断评论是否为垃圾评论，另一个是多分类任务，用于识别垃圾评论的类型。 PhoBERT 在两个任务中都表现最好，分别获得了 86.89％和 72.17％的宏平均 F1 分数。