Abstract
Online reviews significantly influence consumer decisions, making the increasing prevalence of fake reviews in app marketplaces concerning. These deceptive reviews distort the competitive landscape, providing unfair advantages or disadvantages to certain apps. Despite ongoing efforts to detect fake reviews, the sophistication of fake review generation continues to evolve, necessitating continuous improvements in detection models. Current models often focus on precision, potentially overlooking many fake reviews. This research addresses these challenges by developing a machine learning model since experiments on app reviews were published on a popular App Marketplace. The developed model detects fake reviews based on the textual content and the reviewer’s behavior, offering a relevant approach to enhancing the integrity of app marketplaces.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Luca, M., Zervas, G.: Fake it till you make it: reputation, competition, and yelp review fraud. SSRN Electron. J. (62) (2013)
Mayzlin, D., Dover, Y., Chevalier, J.: Promotional reviews: an empirical investigation of online review manipulation. Am. Econ. Rev. 104(8), 2421–2455 (2014)
Wu, Y., Ngai, E., Wu, P.: Fake online reviews: Literature review, synthesis, and directions for future research. Decis. Supp. Syst. 132, 113280 (2020)
Rayana, S., Akoglu, L.: Collective opinion spam detection: Bridging review. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2015)
Kennedy, S., Walsh, N., Sloka, K., Foster, J., McCarren, A.: Fact or factitious? Contextualized opinion spam detection. In:Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (2019)
Mukherjee, A., Liu, B., Glance, N.: Spotting fake reviewer groups in consumer reviews. In: Proceedings of the 21st international conference on World Wide Web (2012)
Martens, D., Maalej, W.: Towards understanding and detecting fake reviews in app stores. Empirical Softw. Eng. (2019). https://doi.org/10.1007/s10664-019-09706-9
Li, S., Guojin, Z.: A deceptive reviews detection method based on multidimensional feature construction and ensemble feature selection. IEEE Trans. Comput. Soc. Syst. (2023)
Saeedreza, S., Mostafa, S., Reza, F., Noel, C.: NetSpam: a network-based spam detection framework for reviews in online social media. IEEE Trans. Inf. Forensics Secur. 12(7), 1585–1595 (2017)
Moosleitner, M., Specht, G., Zangerle, E.: Detection of generated text reviews by leveraging methods from authorship. In: Datenbanksysteme für Business, Technologie und Web , Gesellschaft für Informatik e.V (2023)
Jindal, N., Liu, B.: Opinion spam and analysis. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 219–230. ACM (2008)
Mukherjee, A., Liu, B., Glance, N.: Spotting fake reviewer groups in consumer reviews. In: Proceedings of the 21st International Conference on World Wide Web (2012)
Muchnik, L., Pei, S., Parra, L.C., Reis, S.D., Andrade, J.J.S., Makse, H.A.: Origins of power-law degree distribution in the heterogeneity of human activity in social networks. Sci. Rep. 3(1), 1783 (2013)
McInnes, L., Healy, J.: UMAP: Uniform manifold approximation and projection for dimension reduction. ArXiv e-prints 03426, 2018 (1802)
Elmurngi, E., Gherbi, A.: Detecting Fake Reviews through Sentiment Analysis Using Machine Learning Techniques (2018)
Mukherjee, A., Venkataraman, V.: Classification and Analysis of Real and Pseudo Reviews (2013)
Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 5(4), 1093–1113 (2014)
Ramos, J.: Using TF-IDF to determine word relevance in document queries (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Fiallos, A., Anton, E. (2024). Towards Reliable App Marketplaces: Machine Learning-Based Detection of Fraudulent Reviews. In: Florez, H., Leon, M. (eds) Applied Informatics. ICAI 2023. Communications in Computer and Information Science, vol 1874. Springer, Cham. https://doi.org/10.1007/978-3-031-46813-1_16
Download citation
DOI: https://doi.org/10.1007/978-3-031-46813-1_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-46812-4
Online ISBN: 978-3-031-46813-1
eBook Packages: Computer ScienceComputer Science (R0)