Abstract
Due to the rapid growth of network data, the authenticity and reliability of network information have become increasingly important and have presented challenges. Most of the methods for fake review detection start with textual features and behavioral features. However, they are time-consuming and easily detected by fraudulent users. Although most of the existing neural network-based methods address the problems presented by the complex semantics of reviews, they do not account for the implicit patterns among users, reviews, and products; additionally, they do not consider the usefulness of information regarding fine-grained aspects in identifying fake reviews. In this paper, we propose an attention-based multilevel interactive neural network model with aspect constraints that mines the multilevel implicit expression mode of reviews and integrates four dimensions, namely, users, review texts, products and fine-grained aspects, into review representations. We model the relationships between users and products and use these relationships as a regularization term to redefine the model’s objective function. The experimental results from three public datasets show that the model that we propose is superior to the state-of-the-art methods; thus showing the effectiveness and portability of our model.
Highlights
The Internet is a tool by which people acquire knowledge and a platform on which people can express their views and disseminate information
We found that MIANA based on bi-directional LSTM (Bi-LSTM) and MIANA based on RCNN perform well, due to space limitations, we only analyzes the MIANA based on Bi-LSTM model in this paper
The experimental results of HFAN-A and MIANA represent an improvement upon those of HFAN and MIAN (In Table 4), showing that fine-grained aspect information is falsely discriminatory in the context of reviews and confirming our assumption in this paper that fine-grained aspects can used as a plan to identify fake reviews
Summary
The Internet is a tool by which people acquire knowledge and a platform on which people can express their views and disseminate information. In the realm of e-commerce, review information has a significant impact on both users’ purchasing decisions [1] and enterprises’ development on online platforms. According to the latest data from the social commerce platform Bazaarvoice, more than 50% of users discontinue their purchasing behavior and lose trust in brands after discovering fake reviews of a product. Fake reviews may damage the entire online review system, but cause a loss of credibility [2]. It is important to automatically identify fake reviews on online platforms and provide users with more truthful information. Fake review detection was first named as the opinion spam detection by Jindal and Liu [3]. Due to the important research implications of this work, a number of
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