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Detecting review spammer groups in dynamic review networks

Authors:

Mahmoud DaneshmandAuthors Info & Claims

ACM TURC '19: Proceedings of the ACM Turing Celebration Conference - China

Article No.: 11, Pages 1 - 6

https://doi.org/10.1145/3321408.3323077

Published: 17 May 2019 Publication History

Abstract

Online product reviews are becoming the second most trusted source of product information, second only to recommendations from family and friends, because consumers think that online product reviews reflect recommendations of "real" people. However, in order to maximize the impact, some merchants organize a group of fraudulent reviewers to post a lot of fraudulent reviews that mislead consumers, which is called review spammer group. Solutions for review spammer group detection are very limited, and most solutions focus on static review networks. In this paper, we propose an online two-step framework, called OGSpam, detecting review spammer groups in dynamic review networks. By model a dynamic review network as an initial static review network with an infinite change review stream, our framework first detects reviewer groups on the initial static review network (first snapshot) based on classical Clique Percolation Method (CPM). Then, it detects reviewer groups on snapshot T+1 using reviewer network at T+1 and reviewer groups at T. The experimental results on two real-world review datasets illustrate the efficiency and effectiveness of our framework. To the best of our knowledge, this is the first time to detect review spammer group in dynamic review network.

References

[1]

Feng Zhu and Xiaoquan Zhang. 2010. Impact of online consumer reviews on sales: The moderating role of product and consumer characteristics. Journal of Marketing 74, 2 (2010), 133--148.

[2]

Santosh K C and Arjun Mukherjee. 2016. On the temporal dynamics of opinion spamming: Case studies on yelp. In Proceedings of the 25th International Conference on World Wide Web (WWW). ACM, 369--379.

Digital Library

[3]

Atefeh Heydari, Mohammad ali Tavakoli, Naomie Salim and Zahra Heydari. 2015. Detection of review spam: A survey. Expert Systems with Applications 42, 7 (2015), 3634--3642.

Digital Library

[4]

Emerson F. Cardoso, Renato M. Silva and Tiago A. Almeida. 2018. Towards automatic filtering of fake reviews. Neurocomputing 309 (2018), 106--116.

Digital Library

[5]

Kyung-Hyan Yoo and Ulrike Gretzel. 2009. Comparison of deceptive and truthful travel reviews. In Proceedings on Information and Communication Technologies in Tourism. Springer, 37--47.

[6]

Song Feng, Longfei Xing, Anupam Gogar and Yejin Choi. 2012. Distributional Footprints of Deceptive Product Reviews. In Proceedings of the International Conference on Weblogs and Social Media (ICWSM). AAAI, 98--105.

[7]

Saeedreza Shehnepoor, Mostafa Salehi, Reza Farahbakhsh and Noel Crespi. 2017. NetSpam: A Network-Based Spam Detection Framework for Reviews in Online Social Media. IEEE Transactions on Information Forensics and Security 12, 7 (2017), 1585--1595.

Digital Library

[8]

Geli Fei, Arjun Mukherjee, Bing Liu, Meichun Hsu, Malu Castellanos and Riddhiman Ghosh. 2013. Exploiting Burstiness in Reviews for Review Spammer Detection. In Proceedings of the 7th International Conference on Weblogs and Social Media. AAAI, 175--184.

[9]

Guangxia Xu, Jin Qi, Deling Huang and Mahmoud Daneshmand. 2016. Detecting spammers on social networks based on a hybrid model. In Proceedings of the International Conference on Big Data. IEEE, 3062--3068.

[10]

Guangxia Xu, Jingteng Zhao and Deling Huang. 2016. An improved social spammer detection based on tri-training. In Proceedings of the International Conference on Big Data. IEEE, 4040--4042.

[11]

Zhuo Wang, Tingting Hou, Dawei Song, Zhun Li and Tianqi Kong. 2016. Detecting Review Spammer Groups via Bipartite Graph Projection. Computer Journal 59, 6 (2016), 861--874.

[12]

Rupesh Kumar Dewang and Anil Kumar Singh. 2018. State-of-art approaches for review spammer detection: a survey. Journal of Intelligent Information Systems 50, 2 (2018), 231--264.

Digital Library

[13]

Gergely Palla, Albert-Lszl Barabsi and Tams Vicsek. 2007. Quantifying social group evolution. Nature 446, 7136 (2007), 664--667.

[14]

Sitaram Asur, Srinivasan. Parthasarathy and Duygu Ucar. 2007. An event-based framework for characterizing the evolutionary behavior of interaction graph. ACM Transactions on Knowledge Discovery from Data (TKDD) 3, 4 (2007), 913--921.

Digital Library

[15]

Wei Chen, Zhenming Liu, Xiaorui Sun and Yajun Wang. 2010. A game-theoretic framework to identify overlapping communities in social networks. Data Mining and Knowledge Discovery 21, 2 (2010), 224--240.

Digital Library

[16]

Gergely Palla, Imre Dernyi, Ills Farkas and Tams Vicsek. 2005. Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 7043 (2005), 814--818.

[17]

Arjun Mukherjee, Vivek Venkataraman, Bing Liu, Natalie Glance. 2013. What yelp fake review filter might be doing? In Proceedings of the 7th International Conference on Weblogs and Social Media (ICWSM). AAAI, 409--418.

[18]

Shebuti Rayana and Leman Akoglu. 2015. Collective opinion spam detection: Bridging review networks and metadata. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 985--994.

Digital Library

[19]

Arjun Mukherjee, Abhinav Kumar, Bing Liu, Junhui Wang, Meichun Hsu, Malu Castellanos and Riddhiman Ghosh. 2013. Spotting opinion spammers using behavioral footprints. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 632--640.

Digital Library

[20]

Ee-Peng Lim, Viet-An Nguyen, Nitin Jindal, Bing Liu and Hady W. Lauw. 2010. Detecting product review spammers using rating behaviors. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM). ACM, 939--948.

Digital Library

[21]

Guan Wang, Sihong Xie, Bing Liu and Philip S. Yu. 2011. Review Graph Based Online Store Review Spammer Detection. In Proceedings of the International Conference on Data Mining (ICDM). IEEE, 1242--1247.

Digital Library

[22]

Chang Xu and Jie Zhang. 2015. Towards Collusive Fraud Detection in Online Reviews. In Proceedings of the International Conference on Data Mining (ICDM). IEEE, 1051--1056.

Digital Library

Cited By

Jiang YLiu GWu JLin H(2022)Telecom Fraud Detection via Hawkes-enhanced Sequence ModelIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3150803(1-1)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3150803
Jiang YLiu G(2022)Two-stage anomaly detection algorithm via dynamic community evolution in temporal graphApplied Intelligence10.1007/s10489-021-03109-452:11(12222-12240)Online publication date: 2-Feb-2022
https://doi.org/10.1007/s10489-021-03109-4

Index Terms

Detecting review spammer groups in dynamic review networks
1. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation
    1. Social engineering attacks

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ACM TURC '19: Proceedings of the ACM Turing Celebration Conference - China

May 2019

963 pages

ISBN:9781450371582

DOI:10.1145/3321408

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 May 2019

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ACM TURC 2019

ACM TURC 2019: ACM Turing Celebration Conference - China

May 17 - 19, 2019

Chengdu, China

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Cited By

Jiang YLiu GWu JLin H(2022)Telecom Fraud Detection via Hawkes-enhanced Sequence ModelIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3150803(1-1)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3150803
Jiang YLiu G(2022)Two-stage anomaly detection algorithm via dynamic community evolution in temporal graphApplied Intelligence10.1007/s10489-021-03109-452:11(12222-12240)Online publication date: 2-Feb-2022
https://doi.org/10.1007/s10489-021-03109-4

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