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Fake Review Detection in Big Data Using Parallel BBO

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Document pages: 11 pages

Abstract: Online reviews are increasingly used by the customers while purchasing a product or service. In the last few years, significant growth in the number of fake reviews has been observed due to the increasing competition among the e-commerce sites. Thus, fake review detection is an open and challenging problem. However, the majority of research in this field has focused on sequential algorithms which provide inferior results when scaled on the big datasets. To mitigate this problem, this paper presents a novel parallel bio-geography optimization based method to unfold the fake review detection in the big data environment. The experimental analysis is performed on two standard fake review datasets and compared with K-means and 4 state-of-the-art methods in terms of accuracy. The results affirmed that the proposed method has surpassed all the other considered methods on both the dataset. Further, the parallel performance potency of the proposed method is validated by analyzing the speedup performance.

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