Abstract
In recent years, as the value and credibility of online reviews tend to influence people’s shopping feelings and consumption decisions, various online fake reviews have been constantly emerging. Detecting online fake reviews has attracted widespread attention from both the business and research communities. The existing methods are usually to detect fake reviews on off-the-shelf algorithms using kinds of linguistic and behavioral features respectively. That ignores the fusion of different features and does not take into account that different features have different effects on model performance. In this research, a set of linguistic and non-linguistic features is explored, and an optimal feature subset is selected by using random forest algorithm and the sequential backward selection strategy. Then, a hierarchical neural networks for detecting online fake reviews is empirically proposed, which can learn local and global information from multivariate features. Experimental results show that the model proposed in this paper on multiple datasets is superior to the traditional discrete model and the existing neural network benchmark model, and has a good generalization ability.
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Jiang, C., Zhang, X., Jin, A. (2020). Detecting Online Fake Reviews via Hierarchical Neural Networks and Multivariate Features. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12532. Springer, Cham. https://doi.org/10.1007/978-3-030-63830-6_61
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