TY - GEN
T1 - GSCPM
T2 - 2019 IEEE International Conference on Communications, ICC 2019
AU - Xu, Guangxia
AU - Hu, Mengxiao
AU - Ma, Chuang
AU - Daneshmand, Mahmoud
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - Online product review is becoming one of important reference indicators for people shopping, but the current product review site contains a lot of fraudulent reviews. Group review spamming, which involves a group of fraudulent reviewers writing a lot of fraudulent reviews for one or more target products, becomes the main form of review spamming. However, solutions for group spammer detection are very limited, and due to lack of ground-truth review data, this problem has never been completely solved. In this paper, we propose a novel three-step method to detect group spammers based on Clique Percolation Method (CPM) in a completely unsupervised way, called GSCPM. First, it utilizes clues from behavioral data (timestamp, rating) and relational data (network) to construct a suspicious reviewer graph. Then, it breaks the whole suspicious reviewer graph into k-clique clusters based on CPM, and we consider such k-clique clusters as highly suspicious candidate group spammers. Finally, it ranks candidate groups by group-based and individual-based spam indicators. We use three real-world review datasets from Yelp.com to verify the performance of our proposed method. Experimental results show that our proposed method outperforms four compared methods in terms of prediction precision.
AB - Online product review is becoming one of important reference indicators for people shopping, but the current product review site contains a lot of fraudulent reviews. Group review spamming, which involves a group of fraudulent reviewers writing a lot of fraudulent reviews for one or more target products, becomes the main form of review spamming. However, solutions for group spammer detection are very limited, and due to lack of ground-truth review data, this problem has never been completely solved. In this paper, we propose a novel three-step method to detect group spammers based on Clique Percolation Method (CPM) in a completely unsupervised way, called GSCPM. First, it utilizes clues from behavioral data (timestamp, rating) and relational data (network) to construct a suspicious reviewer graph. Then, it breaks the whole suspicious reviewer graph into k-clique clusters based on CPM, and we consider such k-clique clusters as highly suspicious candidate group spammers. Finally, it ranks candidate groups by group-based and individual-based spam indicators. We use three real-world review datasets from Yelp.com to verify the performance of our proposed method. Experimental results show that our proposed method outperforms four compared methods in terms of prediction precision.
KW - clique percolation method
KW - group spamming detection
KW - online product review
KW - review spam
UR - http://www.scopus.com/inward/record.url?scp=85070210673&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85070210673&partnerID=8YFLogxK
U2 - 10.1109/ICC.2019.8761650
DO - 10.1109/ICC.2019.8761650
M3 - Conference contribution
AN - SCOPUS:85070210673
T3 - IEEE International Conference on Communications
BT - 2019 IEEE International Conference on Communications, ICC 2019 - Proceedings
Y2 - 20 May 2019 through 24 May 2019
ER -