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首页> 外文会议>10th IEEE International Conference on Data Mining Workshops >High-Order Concept Associations Mining and Inferential Language Modeling for Online Review Spam Detection
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High-Order Concept Associations Mining and Inferential Language Modeling for Online Review Spam Detection

机译：在线评论垃圾邮件检测的高阶概念关联挖掘和推理语言建模

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摘要

Despite many incidents about fake online consumer reviews have been reported, very few studies have been conducted to date to examine the trustworthiness of online consumer reviews. One of the reasons is the lack of an effective computational method to separate the untruthful reviews (i.e., spam) from the legitimate ones (i.e., ham) given the fact that prominent spam features are often missing in online reviews. The main contribution of our research work is the development of a novel review spam detection method which is underpinned by an unsupervised inferential language modeling framework. Another contribution of this work is the development of a high-order concept association mining method which provides the essential term association knowledge to bootstrap the performance for untruthful review detection. Our experimental results confirm that the proposed inferential language model equipped with high-order concept association knowledge is effective in untruthful review detection when compared with other baseline methods.
机译：尽管已经报道了许多有关虚假在线消费者评论的事件，但迄今为止，很少进行研究来检查在线消费者评论的可信度。原因之一是，鉴于在线评论中经常会丢失明显的垃圾邮件特征，因此缺乏有效的计算方法将不真实的评论（即垃圾邮件）与合法的评论（即火腿）分开。我们研究工作的主要贡献是开发了一种新颖的审查垃圾邮件检测方法，该方法以无监督的推理语言建模框架为基础。这项工作的另一个贡献是开发了一种高级概念关联挖掘方法，该方法提供了必要的术语关联知识来引导性能以进行不正确的评论检测。我们的实验结果证实，与其他基线方法相比，所提出的具有高级概念联想知识的推论语言模型在不真实的评论检测中是有效的。

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