Adaptive Big Data Analytics for Deceptive Review Detection in Online Social Media

The explosive growth of user-contributed reviews in e-Commerce and online social network sites prompts for the design of novel big data analytics frameworks to cope with such a challenge. The main contributions of our research are twofold. First, we design a novel big data analytics framework that leverages distributed computing and streaming to efficiently process big social media data streams. Second, we apply the proposed framework that is underpinned by a novel parallel co-evolution genetic algorithm to adaptively detect deceptive reviews with respect to different social media contexts. Our experiments show that the proposed big data analytics framework can effectively and efficiently detect deceptive reviews from a big social media data stream, and it outperforms other non-distributed big data analytics solutions. To the best of our knowledge, this is the first successful design of an adaptive big data analytics framework for deceptive review detection under a big data environment.