12th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2019, 29th - 30th Jun 2019, Weihai, China

Research Article

Detecting Deceptive Reviews Utilizing Review Group Model

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  • @INPROCEEDINGS{10.4108/eai.29-6-2019.2282593,
        author={Yuejun  Li and Fangxin  Wang and Shuwu  Zhang and Xiaofei  Niu},
        title={Detecting Deceptive Reviews Utilizing Review Group Model},
        proceedings={12th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2019, 29th - 30th Jun 2019, Weihai, China},
        publisher={EAI},
        proceedings_a={MOBIMEDIA},
        year={2019},
        month={6},
        keywords={deceptive review detection opinion spamming review group detection reviewer collusion},
        doi={10.4108/eai.29-6-2019.2282593}
    }
    
  • Yuejun Li
    Fangxin Wang
    Shuwu Zhang
    Xiaofei Niu
    Year: 2019
    Detecting Deceptive Reviews Utilizing Review Group Model
    MOBIMEDIA
    EAI
    DOI: 10.4108/eai.29-6-2019.2282593
Yuejun Li1,*, Fangxin Wang1, Shuwu Zhang1, Xiaofei Niu2
  • 1: Institute of Automation, Chinese Accademy of Sciences
  • 2: School of Computer Science and Technology,Shandong Jianzhu University
*Contact email: liyuejun2014@ia.ac.cn

Abstract

Online product and store reviews play an important role in product and service recommendation for new customers. However, due to economic or fame reasons, dishonest people are employed to write fake reviews which is also called “opinion spamming” to promote or demote target products and services. Previous research has used text similarity, linguistics, rating patterns, graph relation and other behavior for spammer detection. It is difficult to find fake reviews by a glance of product reviews in time-descending order while It’s more easy to identify fraudulent reviews by checking the list of reviews of reviewers. We propose sieries of novel review grouping models to identify both positive and negative deceptive reviews. The review grouping algorithm can effectively split reviews of reviewer into groups which participate in building new model of review spamming detection. Several new features which are language independent based on group model are constructed. Additionally, we explore the collusion behavior between reviewers to build group collusion model. Experiments and evaluations show that the review group method and relevant models can effectivly improve the precision of 4%-7% in deceptive reviews detection task especially those posted by professional review spammers