Computer Science ›› 2019, Vol. 46 ›› Issue (9): 229-236.doi: 10.11896/j.issn.1002-137X.2019.09.034

• Artificial Intelligence • Previous Articles     Next Articles

Double Cycle Graph Based Fraud Review Detection Algorithm

CHEN Jin-yin, HUANG Guo-han, WU Yang-yang, JIA Cheng-yu   

  1. (College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China)
  • Received:2018-07-27 Online:2019-09-15 Published:2019-09-02

Abstract: Because online reviews of stores can provide customers with a lot of valuable information and greatly affect the credibility of stores,a large number of spam reviews are emerged to disturb the order of market for pro-fit.Many stores or individuals deliberately flatter or denigrate certain stores through fake reviews to achieve their profit objectives.Thus an efficient fraud review detection algorithm is crucial.This paper built a graph filter based on the relationships among users,comments and stores,and obtained the reliability of users,comments and stores through iterative calculation,so as to find fake reviews.Three key questions are brought up:to get more reliable reliability of users,comments and stores,to identify the real reviews effectively,and to detect fake reviews and spammers effectively.In order to improve the reliability of users,comments and stores,a double cycle graph based detection algorithm was proposed to obtain reliable users,comments and stores.In order to find fake reviews and spammers effectively,this paper designed a novel weighted graph filter,through the combination of reliability and obtain reliable,and put forward double cycle filtering detection algorithm.The proposed detection algorithm is applied to Yelp datasets for experiments and proved efficiently in detection of spammers and identifies real reviews.

Key words: Behavior characteristic, Double cycle graph, Graph-based filter, Spam detection, User influence

CLC Number: 

  • TP393.1
[1]LI J,OTT M,CARDIE C,et al.Towards a General Rule forIdentifying Deceptive Opinion Spam[C]//Meeting of the Asso-ciation for Computational Linguistics.Baltimere,USA,2014:1566-1576.
[2]LAU R Y K,LIAO S Y,KWOK C W,et al.Text mining and probabilistic language modeling for online review spam detection[J].ACM Transactions on Management Information Systems,2012,2(4):1-30.
[3]LI F,HUANG M,YANG Y,et al.Learning to identify review spam[C]//International Joint Conference on Artificial Intelligence.AAAI Press,2011:2488-2493.
[4]JINDAL N,LIU B.Opinion spam and analysis[C]//International Conference on Web Search & Data Mining.ACM,2008:219-230.
[5]OTT M,CHOI Y,CARDIE C,et al.Finding deceptive opinion spam by any stretch of the imagination[C]//Meeting of the Association for Computational Linguistics:Human Language Technologies.Association for Computational Linguistics,2011:309-319.
[6]MUKHERJEE A,VENKATARAMAN V,LIU B,et al.Fake review detection:Classification and analysis of real and pseudo reviews[D].Chicago:University of Illinois,2013.
[7]YOO K H,GRETZEL U.Comparison of deceptive and truthful travel reviews[M]//Information and Communication Technologies in Tourism,2009.Vienna:Springer,2009:37-47.
[8]MUKHERJEE A,LIU B,GLANCE N.Spotting fake reviewer groups in consumer reviews[C]//International Conference on World Wide Web.ACM,2012:191-200.
[9]LI F,HUANG M,YANG Y,et al.Learning to identify review spam[C]//International Joint Conference on Artificial Intelligence.AAAI Press,2011:2488-2493.
[10]FEI G,MUKHERJEE A,LIU B,et al.Exploiting burstiness in reviews for review spammer detection[C]//Seventh InternationalAAAI Conference on Weblogs and Social Media.Menlo Park:AAAI press,2013.
[11]LIM E P,NGUYEN V A,JINDAL N,et al.Detecting product review spammers using rating behaviors[C]//Proceedings of the 19th ACM International Conference on Information and Know-ledge Management.ACM,2010:939-948.
[12]XU C,ZHANG J.Combating product review spam campaignsvia multiple heterogeneous pairwise features[C]//Proceedings of the 2015 SIAM International Conference on Data Mining.Society for Industrial and Applied Mathematics,2015:172-180.
[13]YE J,AKOGLU L.Discovering Opinion Spammer Groups by Network Footprints[C]//Joint European Conference on Machine Learning and Knowledge Discovery in Databases.Cham:Springer,2015:97-97.
[14]LI H,CHEN Z,MUKHERJEE A,et al.Analyzing and detecting opinion spam on a large-scale dataset via temporal and spatial patterns[C]//Ninth International AAAI Conference on Web and Social Media.AAAI,2015.
[15]SONG H X,YAN X,YU Z T,et al.Detection of Fake Reviews Based on Adaptive Clustering[J].Journal of Nanjing University(Natural Science),2013,49(4):433-438.(in Chinese)宋海霞,严馨,余正涛,等.基于自适应聚类的虚假评论检测[J].南京大学学报(自然科学版),2013,49(4):433-438.
[16]HUANG J,QIAN T,HE G,et al.Detecting Professional Spam Reviewers[M]//Advanced Data Mining and Applications.Berlin:Springer,2013:288-299.
[17]LI H,FEI G,SHAO W,et al.Bimodal Distribution and Co-Bursting in Review Spam Detection[C]//International Confe-rence on World Wide Web.International World Wide Web Conferences Steering Committee,2017:1063-1072.
[18]YE J,KUMAR S,AKOGLU L.Temporal opinion spam detection by multivariate indicative signals[C]//Tenth International AAAI Conference on Web and Social Media.AAAI,2016.
[19]WANG B,HUANG J,ZHENG H,et al.Semi-Supervised Recursive Autoencoders for Social Review Spam Detection[C]//International Conference on Computational Intelligence and Security.IEEE,2017:116-119.
[20]NARAYAN R,ROUT J K,JENA S K.Review Spam Detection Using Opinion Mining[C]//Progress in Intelligent Computing Techniques:Theory,Practice,and Applications.Singapore:Springer,2018:273-279.
[21]WANG G,XIE S,LIU B,et al.Review Graph Based Online Store Review Spammer Detection[C]//IEEE International Conference on Data Mining.IEEE,2011:1242-1247.
[22]WANG G,XIE S,LIU B,et al.Identify Online Store ReviewSpammers via Social Review Graph[J].ACM Transactions on Intelligent Systems & Technology,2012,3(4):1-21.
[23] WANG Z,LI Z,XU Y,et al.Detecting Product Review Spammers Based on Review Graphs [J].Computer Science,2014,41(10):295-299.(in Chinese)王琢,李准,徐野,等.基于评论图的虚假产品评论人的检测[J].计算机科学,2014,41(10):295-299.
[24]RAYANA S,AKOGLU L.Collective Opinion Spam Detection:BridgingReview Networks and Metadata[C]//ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2015:985-994.
[25]AKOGLU L,CHANDY R,FALOUTSOS C.Opinion fraud detection in online reviews by network effects[C]//Seventh International AAAI Conference on Weblogs and Social Media.2013.
[1] TAN Qi, ZHANG Feng-li, WANG Ting, WANG Rui-jin, ZHOU Shi-jie. Social Network User Influence Evaluation Algorithm Integrating Structure Centrality [J]. Computer Science, 2021, 48(7): 124-129.
[2] TAN Qi, ZHANG Feng-li, ZHANG Zhi-yang, CHEN Xue-qin. Modeling Methods of Social Network User Influence [J]. Computer Science, 2021, 48(2): 76-86.
[3] XU Wei, LIN Bo-gang, LIN Si-juan and YANG Yang. Assessment of User Influence in Social Networks Based on Multi-label Propagation [J]. Computer Science, 2016, 43(10): 135-140.
[4] DU Nan, HAN Lan-sheng, FU Cai, ZHANG Zhong-ke and LIU Ming. Detection of Malware Code Based on Acquaintance Degree [J]. Computer Science, 2015, 42(1): 187-192.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!