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Detecting Fake Reviews Using Multidimensional Representations With Fine-Grained Aspects Plan

机译：使用具有细粒度方面计划的多维陈述来检测假审查

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摘要

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.
机译：由于网络数据的快速增长，网络信息的真实性和可靠性变得越来越重要，并提出了挑战。大多数用于假审查检测的方法，从文本特征和行为特征开始。然而，它们是拖尾用户耗时并且容易检测到的。虽然大多数现有的基于神经网络的方法解决了复杂的评论中的问题，但他们不考虑用户，评论和产品之间的隐含模式;此外，他们不考虑关于识别虚假评论的细粒度方面的信息的有用性。在本文中，我们提出了一种基于关注的多级交互式神经网络模型，具有方面约束，即挖掘多级隐式表达模式的评论，即集成四维，即用户，查看文本，产品和细粒度方面，进入审查陈述。我们模拟了用户和产品之间的关系，并使用这些关系作为正则化术语来重新定义模型的目标函数。三个公共数据集的实验结果表明，我们提出的模型优于最先进的方法;因此，显示了我们模型的有效性和可移植性。

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