Abstract
Given the growing proliferation of social networks in the last couple of years, these online platforms have turned into a virtual space where users disseminate a massive amounts of information in a very short time. However, a considerable amount of such information is false. In fact, spammers usually target these online platforms to spread opinion spams and, thus, affect the decisions of other internet users. To overcome this problem, our objective in this paper is to propose an approach that identifies the distinctive characteristics of opinion spams through the use of a word annotation dictionary that we constructed together with logistic regression.
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
References
Pröllochs, N.: Community-based fact-checking on Twitter’s birdwatch platform. In: Proceedings of the International AAAI Conference on Web and Social Media, pp. 794–805 (2022)
Loi relative à la Lutte contre la Manipulation de l’Information Déclaration annuelle au CSA pour 2019 Facebook Ireland Limited. 23 avril (2020)
Daiv, K., et al.: An approach to detect fake reviews based on logistic regression using review-centric features. Int. Res. J. Eng. Technol. (IRJET) 07(06), 2107–2112 (2020). e-ISSN: 2395–0056
Ram, N.C.S., et al.: Fake reviews detection using supervised machine learning. Int. J. Res. Appl. Sci. Eng. Technol. (IJRASET) 10(V), 3718–3727, May 2022
Dedeturk, B.K., Akay, B.: Spam filtering using a logistic regression model trained by an artificial bee colony algorithm. Appl. Soft Comput. 91, 106229 (2020). ISSN 1568–4946
Kontsewaya, Y., Antonov, E., Artamonov, A.: Evaluating the effectiveness of machine learning methods for spam detection. Procedia Comput. Sci. 190, 479–486 (2021)
Kumar, N., Venugopal, D., Qiu, L., Kumar, S.: Detecting review manipulation on online platforms with hierarchical supervised learning. J. Manag. Inf. Syst. 35(1), 350–380 (2018). https://doi.org/10.1080/07421222.2018.1440758
Bsoul, M.A., Qusef, A., Abu-Soud, S.: Building an optimal dataset for Arabic fake news detection. Procedia Comput. Sci. 201, 665–672 (2022). ISSN 1877-0509
Bensouda, N., El Fkihi, S., Faizi, R.: Opinion spam detection: a review of the literature. In: Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications, pp. 1–6 (May 2018)
Ott, M., Cardie, C., Hancock, J.T.: Negative deceptive opinion spam. In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies 2013
Cambridge advanced learner’s dictionary 4th edition
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Bensouda, N., Fkihi, S.E., Faizi, R. (2023). Extracting the Distinctive Features of Opinion Spams Using Logistic Regression. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 715. Springer, Cham. https://doi.org/10.1007/978-3-031-35507-3_50
Download citation
DOI: https://doi.org/10.1007/978-3-031-35507-3_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-35506-6
Online ISBN: 978-3-031-35507-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)