Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Security Analysis of SM9 Key Agreement and Encryption Read chapter Read first chapter

2019 | OriginalPaper | Chapter

SpamTracer: Manual Fake Review Detection for O2O Commercial Platforms by Using Geolocation Features

Authors : Ruoyu Deng, Na Ruan, Ruidong Jin, Yu Lu, Weijia Jia, Chunhua Su, Dandan Xu

Published in: Information Security and Cryptology

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Nowadays, O2O commercial platforms are playing a crucial role in our daily purchases. However, some people are trying to manipulate the online market maliciously by opinion spamming, a kind of web fraud behavior like writing fake reviews, due to fame and profits, which will harm online purchasing environment and should be detected and eliminated. Moreover, manual fake reviewers are more deceptive compared with old web spambots. Although several efficient methods were proposed in the fake review detection field, the manual fake reviewers are also evolving rapidly. They imitate to be benign users to control the velocity of review fraud actions, and deceive the detection system. Our investigation presented that geolocation factor is potential and can well reflect the distinctions between fake reviewers and benign users. In this research, we analyzed the geolocations of shops in reviews, found the distinct distribution features of those in fake reviewers and benign users, and proposed a SpamTracer model that can identify fake reviewers and benign users by exploiting an improved HMM (Hidden Markov Model). Our experiment demonstrated that SpamTracer could achieve 71% accuracy and 76% recall in the unbalanced dataset, outperforming some excellent classical approaches in the aspect of stability. Furthermore, SpamTracer can help to analyze the regularities of review fraud actions. Those regularities reflect the time and location in which online shops are likely to hire fake reviewers to increase their turnover. We also found that a small group of fake reviewers tend to work with plural shops located in a small business zone.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Blockchain-Based Privacy Preserving Deep Learning
next chapter A Light-Weight and Accurate Method of Static Integer-Overflow-to-Buffer-Overflow Vulnerability Detection
Footnotes
Literature
1.
Agrawal, M., Leela Velusamy, R.: Unsupervised spam detection in hyves using SALSA. In: Das, S., Pal, T., Kar, S., Satapathy, S.C., Mandal, J.K. (eds.) Proceedings of the 4th International Conference on Frontiers in Intelligent Computing: Theory and Applications (FICTA) 2015. AISC, vol. 404, pp. 517–526. Springer, New Delhi (2016). https://​doi.​org/​10.​1007/​978-81-322-2695-6_​43CrossRef
2.
Castillo, C., Donato, D., Gionis, A., Murdock, V., Silvestri, F.: Know your neighbors: web spam detection using the web topology. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Amsterdam, The Netherlands, pp. 423–430 (2007)
3.
Chakraborty, M., Pal, S., Pramanik, R., Chowdary, C.R.: Recent developments in social spam detection and combating techniques: a survey. Inf. Process. Manage. 52, 1053–1073 (2016)CrossRef
4.
Chen, C., Wu, K., Srinivasan, V., Zhang, X.: Battling the internet water army: detection of hidden paid posters. In: 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Niagara Falls, Canada, pp. 116–120 (2013)
5.
Chen, H., He, D., Zhu, S., Yang, J.: Toward detecting collusive ranking manipulation attackers in mobile app markets. In: Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security, Abu Dhabi, UAE, pp. 58–70 (2017)
6.
Chino, D.Y.T., Costa, A.F., Traina, A.J.M., Faloutsos, C.: VolTime: unsupervised anomaly detection on users’ online activity volume. In: Proceedings of the 2017 SIAM International Conference on Data Mining, Houston, USA, pp. 108–116 (2017)
7.
Gong, Q., et al.: Deepscan: exploiting deep learning for malicious account detection in location-based social networks. In: IEEE Communications Magazine, Feature Topic on Mobile Big Data for Urban Analytics, vol. 56 (2018)CrossRef
8.
Hooi, B., Song, H.A., Beutel, A., Shah, N., Shin, K., Faloutsos, C.: Fraudar: bounding graph fraud in the face of camouflage. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, USA, pp. 895–904 (2016)
9.
Jindal, N., Liu, B.: Opinion spam and analysis. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, New York, USA, pp. 219–230 (2008)
10.
KC, S., Mukherjee, A.: On the temporal dynamics of opinion spamming: case studies on yelp. In: Proceedings of the 25th International Conference on World Wide Web, pp. 369–379. Republic and Canton of Geneva, Switzerland (2016)
11.
Kumar, S., Gao, X., Welch, I., Mansoori, M.: A machine learning based web spam filtering approach. In: IEEE 30th International Conference on Advanced Information Networking and Applications, Crans-Montana, Switzerland, pp. 973–980 (2016)
12.
Li, H., Chen, Z., Liu, B., Wei, X., Shao, J.: Spotting fake reviews via collective positive-unlabeled learning. In: 2014 IEEE International Conference on Data Mining, Shenzhen, China, pp. 899–904 (2014)
13.
Li, H., et al.: Bimodal distribution and co-bursting in review spam detection. In: Proceedings of the 26th International Conference on World Wide Web, pp. 1063–1072. Republic and Canton of Geneva, Switzerland (2017)
14.
Malmgren, R.D., Hofman, J.M., Amaral, L.A., Watts, D.J.: Characterizing individual communication patterns. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2009, New York, USA, pp. 607–616 (2009)
15.
Mukherjee, A., Liu, B., Glance, N.: Spotting fake reviewer groups in consumer reviews. In: Proceedings of the 21st International Conference on World Wide Web, Lyon, France, pp. 191–200 (2012)
16.
Nilizadeh, S., et al.: Poised: spotting twitter spam off the beaten paths. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, Dallas, USA, pp. 1159–1174 (2017)
17.
Ott, M., Choi, Y., Cardie, C., Hancock, J.T.: Finding deceptive opinion spam by any stretch of the imagination. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Stroudsburg, USA, vol. 1, pp. 309–319 (2011)
18.
Phang, C.W., Tan, C.H., Sutanto, J., Magagna, F., Lu, X.: Leveraging O2O commerce for product promotion: an empirical investigation in mainland China. IEEE Trans. Eng. Manage. 61, 623–632 (2014)CrossRef
19.
Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. Proc. IEEE 77, 257–286 (1989)CrossRef
20.
Shojaee, S., Murad, M.A.A., Azman, A.B., Sharef, N.M., Nadali, S.: Detecting deceptive reviews using lexical and syntactic features. In: 13th International Conference on Intellient Systems Design and Applications, Malaysia, pp. 53–58 (2013)
21.
Spirin, N., Han, J.: Survey on web spam detection: principles and algorithms. SIGKDD Explor. Newslett. 13, 50–64 (2012)CrossRef
22.
Washha, M., Qaroush, A., Mezghani, M., Sedes, F.: A topic-based hidden markov model for real-time spam tweets filtering. Procedia Comput. Sci. 112, 833–843 (2017)CrossRef
23.
Yao, W., Ruan, N., Yu, F., Jia, W., Zhu, H.: Privacy-preserving fraud detection via cooperative mobile carriers with improved accuracy. In: 2017 14th Annual IEEE International Conference on Sensing. Communication, and Networking, San Diego, USA, pp. 1–9 (2017)
24.
Yao, Y., Viswanath, B., Cryan, J., Zheng, H., Zhao, B.Y.: Automated crowdturfing attacks and defenses in online review systems. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, Dallas, USA, pp. 1143–1158 (2017)
25.
Zhang, X., Zheng, H., Li, X., Du, S., Zhu, H.: You are where you have been: sybil detection via geo-location analysis in OSNs. In: 2014 IEEE Global Communications Conference, Austin, USA, pp. 698–703 (2014)
26.
Zheng, H., et al.: Smoke screener or straight shooter: detecting elite sybil attacks in user-review social networks. In: The 2018 Network and Distributed System Security Symposium, San Diego, USA (2018)
Metadata
Title
SpamTracer: Manual Fake Review Detection for O2O Commercial Platforms by Using Geolocation Features
Authors
Ruoyu Deng
Na Ruan
Ruidong Jin
Yu Lu
Weijia Jia
Chunhua Su
Dandan Xu
Copyright Year
2019
Book
Information Security and Cryptology

Print ISBN: 978-3-030-14233-9

Electronic ISBN: 978-3-030-14234-6

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-14234-6_21

Premium Partner

    Image Credits