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Research Article

Fake Profile Detection Using Logistic Regression and Gradient Descent Algorithm on Online Social Networks

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  • @ARTICLE{10.4108/eetsis.4342,
        author={Eswara Venkata Sai Raja and Bhrugumalla L V S Aditya and Sachi Nandan Mohanty},
        title={Fake Profile Detection Using Logistic Regression and Gradient Descent Algorithm on Online Social Networks},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={11},
        number={1},
        publisher={EAI},
        journal_a={SIS},
        year={2023},
        month={11},
        keywords={social media, spammers, detection, logistic regression, optimization algorithm, gradient descent, accuracy, precision},
        doi={10.4108/eetsis.4342}
    }
    
  • Eswara Venkata Sai Raja
    Bhrugumalla L V S Aditya
    Sachi Nandan Mohanty
    Year: 2023
    Fake Profile Detection Using Logistic Regression and Gradient Descent Algorithm on Online Social Networks
    SIS
    EAI
    DOI: 10.4108/eetsis.4342
Eswara Venkata Sai Raja1, Bhrugumalla L V S Aditya1,*, Sachi Nandan Mohanty1
  • 1: Vellore Institute of Technology University
*Contact email: aditya.22phd7023@vitap.ac.in

Abstract

One of the most challenging issues on online social networks is identifying spam accounts. The concern stems from the fact that these personas pose a significant threat, as they may engage in harmful activities against other users, extending beyond mere annoyance or low-quality advertisements. The demand for accurate and effective spam detection algorithms for online social networks is increasing due to this risk. To address the problem of spam detection in online social networks, this research proposes a hybrid machine learning model based on logistic regression and a contemporary metaheuristic method called the Gradient Descent Algorithm. The proposed approach automates spammer identification and provides insights into the factors that have the greatest impact on the detection process. Additionally, the model is evaluated and implemented on multiple datasets, and the experiments and findings demonstrate that the proposed model outperforms many other algorithms in terms of accuracy and delivers robust results in terms of precision, recall, f-measure, and AUC. It also aids in identifying the factors that influence detection the most.

Keywords
social media, spammers, detection, logistic regression, optimization algorithm, gradient descent, accuracy, precision
Received
2023-07-27
Accepted
2023-10-31
Published
2023-11-09
Publisher
EAI
http://dx.doi.org/10.4108/eetsis.4342

Copyright © 2023 E. V. Sai Raja et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NCSA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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