A Machine Learning approach for Fake Profile Classification in Social Networking

Sneha A; Boopathi Kumar E

doi:10.55524/ijircst.2025.13.2.14

Abstract

This study suggests a machine learning-based detection system built with Python and Django to tackle the growing problem of fraudulent profiles on social networking sites. Malicious actors are progressively setting up phony identities for spamming, phishing, and disseminating false information as social media usage keeps growing. In order to accurately identify bogus profiles, the suggested system analyzes user attributes, behavioral patterns, and network properties using a variety of supervised learning algorithms, such as Random Forests, Support Vector Machines, and Decision Trees. Our approach makes advantage of Django's powerful web framework to produce an intuitive, scalable profile monitoring and analysis interface. According to experimental data, the overall detection accuracy is 92%, with 90% precision and 88% recall rates. The system greatly outperforms traditional rule-based approaches in both detection accuracy and processing efficiency, particularly when handling large datasets. The Django implementation provides real-time monitoring capabilities, reducing manual verification efforts while maintaining high detection reliability. This research contributes to enhancing online security by providing an effective tool for identifying and mitigating fake profile threats on social networking platforms.

Keywords

Fake Profile Detection, Machine Learning, Social Media Security, Random Forest, Support Vector Machine, Django, Classification Algorithms, Supervised Learning, Data Real-time Detection.

References

B. Goyal, N. S. Gill, and P. Gulia, “Securing social spaces: machine learning techniques for fake profile detection on instagram,” Social Network Analysis and Mining, vol. 14, no. 1, pp. 1–14, 2024. Available form: https://tinyurl.com/yc3e7sxc
B. Goyal, N. S. Gill, P. Gulia, O. Prakash, I. Priyadarshini, R. Sharma, et al., “Detection of fake accounts on social media using multimodal data with deep learning,” IEEE Transactions on Computational Social Systems, 2023. Available form: https://doi.org/10.1109/TCSS.2023.3296837
C. Xiao, D. M. Freeman, and T. Hwa, “Detecting clusters of fake accounts in online social networks,” in Proc. 8th ACM Workshop on Artificial Intelligence and Security, Oct. 2015, pp. 91–101. Available form: https://doi.org/10.1145/2808769.2808779
S. U. Balvir, M. M. Raghuwanshi, and P. D. Shobhane, “Link Prediction in Social Networks: A Dual Perspective on Positive and Negative Links,” in 2024 Asian Conf. on Intelligent Technologies (ACOIT), Sept. 2024, pp. 1–7. Available form: https://doi.org/10.1109/ACOIT62457.2024.10939432
A. Breuer, R. Eilat, and U. Weinsberg, “Friend or faux: Graph-based early detection of fake accounts on social networks,” in Proc. Web Conf. 2020, Apr. 2020, pp. 1287–1297. Available form: https://doi.org/10.1145/3366423.3380204
M. Tsikerdekis, “Real-time identity-deception detection techniques for social media: optimizations and challenges,” IEEE Internet Computing, vol. 22, no. 5, pp. 35–45, 2017. Available form: https://doi.org/10.1109/MIC.2017.265102442
A. Dey, R. Z. Rafi, S. H. Parash, S. K. Arko, and A. Chakrabarty, “Fake news pattern recognition using linguistic analysis,” in 2018 Joint 7th Int. Conf. on Informatics, Electronics & Vision (ICIEV) and 2nd Int. Conf. on Imaging, Vision & Pattern Recognition (icIVPR), Jun. 2018, pp. 305–309. Available form: https://doi.org/10.1109/ICIEV.2018.8641018
S. Adikari and K. Dutta, “Identifying Fake Profiles in Twitter: A Hybrid Approach Using Behavioral Analysis and Metadata,” Social Network Analysis and Mining, vol. 10, no. 1, pp. 1–15, 2020. Available form: https://doi.org/10.48550/arXiv.2006.01381
A. H. Gough and P. A. Johnston, “Requirements, features, and performance of high content screening platforms,” in High Content Screening: A Powerful Approach to Systems Cell Biology and Drug Discovery, pp. 41–61, 2006. Available form: https://tinyurl.com/3um6r5s5
K. Duisebekova, R. Khabirov, and A. Zholzhan, “Django as Secure Web-Framework in Practice,” ??????? ????????? ???????? ?????????? ? ???????????? ??. ?. ??????????, no. 1, pp. 275–281, 2021. Available form: https://doi.org/10.1016/j.engappai.2025.110525
P. Wang, T. M. Berzin, J. R. G. Brown, S. Bharadwaj, A. Becq, X. Xiao, et al., “Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study,” Gut, vol. 68, no. 10, pp. 1813–1819, 2019. Available form: https://doi.org/10.1136/gutjnl-2018-317500
I. B. Adhikari, “A Platform-Agnostic Deployment Framework for Machine Learning Models: A Case Study with an Image Accessibility Application,” Master’s thesis, Oslo Metropolitan University, 2024. Available form: https://tinyurl.com/yy7fc9jm
B. Afzal, M. Umair, G. A. Shah, and E. Ahmed, “Enabling IoT platforms for social IoT applications: Vision, feature mapping, and challenges,” Future Generation Computer Systems, vol. 92, pp. 718–731, 2019. Available form: https://doi.org/10.1016/j.future.2017.12.002

Cites this article as

S. A, B. K. E, "A Machine Learning approach for Fake Profile Classification in Social Networking", International Journal of Innovative Research in Engineering & Management (ijircst), Vol-13, Issue-2, Page No-96-102, 2025. Available from: https://doi.org/10.55524/ijircst.2025.13.2.14

Corresponding Author

Sneha A

Student, Msc -Information Technology , Bharathiar University , Coimbatore , India

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