Skip to main content
SpringerLink
Log in

Review Trade: Everything Is Free in Incentivized Review Groups

  • Conference paper
  • First Online:
Security and Privacy in Communication Networks (SecureComm 2020)

Abstract

Online reviews play a crucial role in the ecosystem of e-commerce business. To manipulate consumers’ opinions, some sellers of e-commerce platforms outsource opinion spamming with incentives (e.g., free products) in exchange for incentivized reviews. As incentives, by nature, are likely to drive more biased reviews or even fake reviews. Despite e-commerce platforms such as Amazon have taken initiatives to squash the incentivized review practice, sellers turn to various social networking platforms (e.g., Facebook) to outsource the incentivized reviews. The aggregation of sellers who request incentivized reviews and reviewers who seek incentives forms incentivized review groups. In this paper, we focus on the incentivized review groups in e-commerce platforms. We perform data collections from various social networking platforms, including Facebook, WeChat, and Douban. A measurement study of incentivized review groups is conducted with regards to group members, group activities, and products. To identify the incentivized review groups, we propose a new detection approach based on co-review graphs. Specifically, we employ the community detection method to find suspicious communities from co-review graphs. Also, we build a “gold standard” dataset from the data we collected, which contains the information of reviewers who belong to incentivized review groups. We utilize the “gold standard” dataset to evaluate the effectiveness of our detection approach.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Amazon bans incentivized reviews (2016). https://www.usatoday.com/story/tech/news/2016/10/03/amazon-bans-incentivized-reviews/91488702/

  2. Amazon consumer review policy (2016). https://www.amazon.com/gp/help/customer/display.html?nodeId=201967050

  3. Consumer review survey (2017). https://www.brightlocal.com/research/local-consumer-review-survey/

  4. Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theo. Exp. 2008(10), P10008 (2008)

    Article  Google Scholar 

  5. Cai, Q., Filos-Ratsikas, A., Tang, P., Zhang, Y.: Reinforcement mechanism design for fraudulent behaviour in e-commerce. In: The 32nd AAAI Conference on Artificial Intelligence (2018)

    Google Scholar 

  6. Chen, H., He, D., Zhu, S., Yang, J.: Toward detecting collusive ranking manipulation attackers in mobile app markets. In: The 2017 ACM on Asia Conference on Computer and Communications Security (2017)

    Google Scholar 

  7. Feng, S., Banerjee, R., Choi, Y.: Syntactic stylometry for deception detection. In: The 50th Annual Meeting of the Association for Computational Linguistics: Short Papers, vol. 2, pp. 171–175. Association for Computational Linguistics (2012)

    Google Scholar 

  8. Fusilier, D.H., Montes-y-Gómez, M., Rosso, P., Cabrera, R.G.: Detection of opinion spam with character n-grams. In: Gelbukh, A. (ed.) CICLing 2015. LNCS, vol. 9042, pp. 285–294. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18117-2_21

    Chapter  Google Scholar 

  9. Harris, C.G.: Detecting deceptive opinion spam using human computation. In: Workshops at the Twenty-Sixth AAAI Conference on Artificial Intelligence (2012)

    Google Scholar 

  10. He, R., McAuley, J.: Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering. In: Proceedings of the 25th International Conference on World Wide Web (WWW), pp. 507–517 (2016)

    Google Scholar 

  11. Jamshidi, S., Rejaie, R., Li, J.: Trojan horses in amazon’s castle: understanding the incentivized online reviews. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (2018)

    Google Scholar 

  12. Kleinberg, J.: Bursty and hierarchical structure in streams. Data Min. Knowl. Disc. 7(4), 373–397 (2003)

    Article  MathSciNet  Google Scholar 

  13. Li, J., Ott, M., Cardie, C., Hovy, E.: Towards a general rule for identifying deceptive opinion spam. In: The 52nd Annual Meeting of the Association for Computational Linguistics, vol. 1 (2014)

    Google Scholar 

  14. Li, S., Caverlee, J., Niu, W., Kaghazgaran, P.: Crowdsourced app review manipulation. In: The 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (2017)

    Google Scholar 

  15. Mukherjee, A., Liu, B., Glance, N.: Spotting fake reviewer groups in consumer reviews. In: The 21st International Conference on World Wide Web (WWW) (2012)

    Google Scholar 

  16. Mukherjee, A., Venkataraman, V., Liu, B., Glance, N.S.: What yelp fake review filter might be doing? In: The International AAAI Conference on Web and Social Media (ICWSM) (2013)

    Google Scholar 

  17. Ni, J., Li, J., McAuley, J.: Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In: The Empirical Methods in Natural Language Processing and International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (2019)

    Google Scholar 

  18. Ott, M., Cardie, C., Hancock, J.T.: Negative deceptive opinion spam. In: The 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2013)

    Google Scholar 

  19. Ott, M., Choi, Y., Cardie, C., Hancock, J.T.: Finding deceptive opinion spam by any stretch of the imagination. In: The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1. Association for Computational Linguistics (2011)

    Google Scholar 

  20. Palla, G., Derényi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043), 814 (2005)

    Article  Google Scholar 

  21. Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76(3), 036106 (2007)

    Article  Google Scholar 

  22. Rayana, S., Akoglu, L.: Collective opinion spam detection: bridging review networks and metadata. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2015)

    Google Scholar 

  23. Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. Proc. Nat. Acad. Sci. 105(4), 1118–1123 (2008)

    Article  Google Scholar 

  24. Wang, G., Xie, S., Liu, B., Philip, S.Y.: Review graph based online store review spammer detection. In: 2011 IEEE 11th International Conference on Data Mining (2011)

    Google Scholar 

  25. Xie, S., Wang, G., Lin, S., Yu, P.S.: Review spam detection via temporal pattern discovery. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2012)

    Google Scholar 

  26. Xie, Z., Zhu, S.: AppWatcher: unveiling the underground market of trading mobile app reviews. In: The 8th ACM Conference on Security & Privacy in Wireless and Mobile Networks, p. 10 (2015)

    Google Scholar 

  27. Xie, Z., Zhu, S., Li, Q., Wang, W.: You can promote, but you can’t hide: large-scale abused app detection in mobile app stores. In: The 32nd Annual Conference on Computer Security Applications (2016)

    Google Scholar 

  28. Xu, H., Liu, D., Wang, H., Stavrou, A.: E-commerce reputation manipulation: the emergence of reputation-escalation-as-a-service. In: International Conference on World Wide Web (WWW), pp. 1296–1306 (2015)

    Google Scholar 

  29. 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 (2017)

    Google Scholar 

  30. Zheng, H., et al.: Smoke screener or straight shooter: detecting elite Sybil attacks in user-review social networks. In: NDSS (2018)

    Google Scholar 

Download references

Acknowledgment

We would like to thank our shepherd Mohammad Mannan and the anonymous reviewers for their detailed and insightful comments, which help to improve the quality of this paper. This work was supported in part by the U.S. ARO grant W911NF-19-1-0049 and NSF grant DGE-1821744.

Author information

Authors and Affiliations

  1. University of Delaware, Newark, DE, USA

    Yubao Zhang

  2. Old Dominion University, Norfolk, VA, USA

    Shuai Hao

  3. Virginia Tech, Arlington, VA, USA

    Haining Wang

Authors
  1. Yubao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuai Hao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Haining Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shuai Hao .

Editor information

Editors and Affiliations

  1. Yonsei University, Seoul, Korea (Republic of)

    Noseong Park

  2. George Mason University, Fairfax, VA, USA

    Kun Sun

  3. Dipartimento di Informatica, Universita degli Studi, Milan, Milano, Italy

    Sara Foresti

  4. University of Florida, Gainesville, FL, USA

    Kevin Butler

  5. Division of Nephrology, University of Alabama, Birmingham, AL, USA

    Nitesh Saxena

Rights and permissions

Reprints and permissions

Copyright information

© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, Y., Hao, S., Wang, H. (2020). Review Trade: Everything Is Free in Incentivized Review Groups. In: Park, N., Sun, K., Foresti, S., Butler, K., Saxena, N. (eds) Security and Privacy in Communication Networks. SecureComm 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 335. Springer, Cham. https://doi.org/10.1007/978-3-030-63086-7_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-63086-7_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63085-0

  • Online ISBN: 978-3-030-63086-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation