# Detection of Chinese Deceptive Reviews Based on Pre-Trained Language Model
## 1 Introduction  
With the development of the Internet, e-commerce platforms have become the mainstream method for consumers to purchase products. Therefore, purchasing goods on the Internet, rather than in physical stores, has become an increasingly popular preference for consumers. However, because consumers do not have direct physical access to the goods, they rely on online product reviews to overcome information asymmetry in their purchasing decisions [1]. These reviews enhance the product value as a key consumer reference. A 2018 study [2] found that 82% of consumers believed that product reviews influenced their purchase decisions. For sellers, product reviews are a method of online marketing whereby positive reviews facilitate sales while negative reviews hinder sales, as demonstrated in a 2013 study [3]. By capitalizing on the business potential found in customer feedback, some sellers started writing false positive reviews to boost their credibility or write false negative reviews to discredit rivals. This practice, which is known as \"deceptive reviews,\" can be found both in Taiwan and other countries. In 2013, Samsung Taiwan was criticized for publishing \"fake reviews\" [4]. Samsung was found to ask its employees to write favorablereviews on Samsung cellphones and negative reviews on the products of its competitor HTC in Taiwan's largest lifestyle and product forum website Mobile01.com (accessed on 22 March 2022). In another example, the TripAdvisor annual review report in 2019 [5] revealed that out of 60 million reviews, 1.38 million were deceptive. Because deceptive reviews provide false information that misleads consumers and intensifies vicious rivalry among sellers, effective methods of detecting such reviews are necessary. The use of machine learning and deep learning to identify deceptive reviews has been explored in past studies [6; 7; 8; 9]. One of the disadvantages highlighted in the literature is that classification in machine learning relies on prior knowledge of experts, which limits the classification efficiency of the model on features selected by experts. Although deep learning does not rely on the prior knowledge of experts, the method encounters a bottleneck when used to interpret texts because of the use of polysemous terms, which results in two current challenges when detecting deceptive reviews. First, a difficulty exists in deciphering polysemous words. For example, the word \" (apple)\" in the sentences \" (Apples are delicious)\" and \" \" (I bought an Apple iPhone)\" conveys different meanings. In the first sentence \"apple\" refers to the fruit, whereas in the second sentence, Apple refers to a brand. However, in both situations, the word \" \" (apple)\" is represented by the same set of word vectors. Second, because of the high costs and low efficiency in manually labeling deceptive reviews, pre-labeled datasets are generally unavailable. In addition, because of the substantial differences between the proportion of real and deceptive reviews in dataset samples, the training process would likely be biased toward one category over the other, thereby affecting the classification efficiency of the model. The present study specifically focuses on the analysis of Chinese text because the majority of existing methods generally perform well when applied to the English language but generally perform very poorly when applied to Chinese language contexts. The reasons for such a discrepancy are the following. First, the word boundaries between the two languages are different. For example, in the English sentence \"I like to eat an apple,\" a space can be placed between each pair of words to indicate the word boundaries, which can easily be recognized by the machine-learning model. However, in the Chinese sentence \" (his talent is extraordinary),\" the characters are joined without spaces and none of the characters serves as a distinct word boundary. In this situation, tokenizers are required to separate the words, which are highly contextual.