Abstract
Day by day people shopping via E-commerce sites is burgeoning. Decision of placing orders relies on product and service reviews provided by the customers. The importance of the reviews has increased tremendously because they provide information about the quality of product and service. This stimulates sellers to exploit these reviews to increase their sales by deceiving the customers with false information. Thus, detection and prevention of fake reviews becomes pivotal. This paper focuses on detecting and preventing fake reviews using classification and authentication techniques. Review content and Reviewer behavior-based features were used to train different machine learning algorithms such as SVM, Random forest, and Decision tree; among these, Random forest classification algorithm had the highest accuracy of fake review detection with 73.33%. Prevention of fake reviews is achieved by making sure the right person gets to write the review by sending the review writing link to the registered email-id. This research is also concerned over preventing bots to write review by examining the keyboard and mouse activities of the machine. Captcha authentication method has been adopted to prevent bots from writing reviews.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Statista—Global retail e-commerce market size 2014–2021. https://www.statista.com/statistics/379046/worldwide-retail-e-commerce-sales/. Last accessed 6 May 2019
Marketing Land—Sterling, G., Sterling, G.: Study finds 61 percent of electronics reviews on Amazon are ‘fake’. https://marketingland.com/study-finds-61-percent-of-electronics-reviews-on-amazon-are-fake-254055. Last accessed 11 May 2019
Dwoskin, E., Timberg, C.: How merchants use Facebook to flood Amazon with fake reviews. The Washington Post. https://www.washingtonpost.com/business/economy/how-merchants-secretly-use-facebook-to-flood-amazon-with-fake-reviews/2018/04/23/5dad1e30–4392-11e8-8569-26fda6b404c7_story.html. Last accessed 10 May 2019
The Drum—Fullerton, L.: Online reviews impact purchasing decisions for over 93% of consumers, report suggests. https://www.thedrum.com/news/2017/03/27/online-reviews-impact-purchasing-decisions-over-93-consumers-report-suggests. Last accessed 11 May 2019
von Helversen, B., Abramczuk, K., Kopeć, W., Nielek, R.: Influence of consumer reviews on online purchasing decisions in older and younger adults. Decis. Support Syst. 113, 1–10 (2018)
Mukherjee, A., Venkataraman, V., Liu, B., Glance, N.: Fake review detection: classification and analysis of real and pseudo reviews. Technical report. UIC-C S-03–2013 (2013)
Ott, M., Choi, Y., Cardie, C., Hancock, J.T.: Finding deceptive opinion spam by any stretch of the imagination. In: ACL, pp. 309–319 (2011)
Mukherjee, A., Kumar, A., Liu, B., et al.: Spotting opinion spammers using behavioral footprints. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 632–640 (2013)
Paolacci, G., Chandler, J., Ipeirotis, P.G.: Running experiments on amazon mechanical turk. Judgm. Decis. Mak. 5(5), 411–419 (2010)
Li, Y., Feng, X., Zhang, S.: Detecting fake reviews utilizing semantic and emotion model. In: 3rd International Conference on Information Science and Control Engineering (ICISCE), pp. 317–320. IEEE (2016)
Feng, S., Banerjee, R., Choi, Y.: Syntactic stylometry for deception detection. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers-Volume 2, pp. 171–175. Association for Computational Linguistics (2012)
Chowdhary, N.S., Pandit, A.A.: Fake review detection using classification. Int. J. Comput. Appl. 975, 8887
McAuley, J.: Amazon review data. http://jmcauley.ucsd.edu/data/amazon/links.html. Last accessed 17 April 2019
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Prakash, P., Shashank, N., Arjun, M., Yadav, P.S.S., Shreyamsa, S.M., Prazwal, N.R. (2020). Fake Review Prevention Using Classification and Authentication Techniques. In: Tuba, M., Akashe, S., Joshi, A. (eds) ICT Systems and Sustainability. Advances in Intelligent Systems and Computing, vol 1077. Springer, Singapore. https://doi.org/10.1007/978-981-15-0936-0_42
Download citation
DOI: https://doi.org/10.1007/978-981-15-0936-0_42
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0935-3
Online ISBN: 978-981-15-0936-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)