An Integrated Machine Learning Framework for Spammer and Fake User Detection in Online Social Networks
DOI:
https://doi.org/10.69996/fmep.2025015Keywords:
Online Social Networks (OSNs), Fake User Detection, Spammer Detection, Machine Learning, Behavioral AnalysisAbstract
This paper proposes a robust machine learning-based framework for detecting spammers and fake users within Online Social Networks (OSNs), addressing the critical challenges of security, privacy, and authenticity in modern social platforms. By analyzing a rich set of behavioral and profile features—such as account age, posting frequency, follower-to-following ratio, and content characteristics—the system leverages ensemble classifiers like Random Forest to accurately distinguish between genuine and fraudulent accounts. The framework integrates explainable AI techniques to provide transparency and interpretability of detection decisions, while privacy-preserving mechanisms ensure compliance with data protection regulations. Extensive simulations demonstrate the model’s high accuracy, precision, recall, and low error rates, outperforming traditional classifiers and adapting effectively to evolving threats. This research offers a scalable, real-time, and trustworthy solution that enhances the security and user experience of OSNs, contributing significantly to ongoing efforts in combating fake accounts and spam on social media platforms. The proposed system analyzes key features such as account age, posting frequency, follower-to-following ratio, and content patterns, employing ensemble methods like Random Forest for classification. Simulation results demonstrate that the Random Forest model achieves an accuracy of 96.8%, precision of 95.7%, recall of 96.2%, and an F1-score of 95.9%, outperforming other classifiers such as Decision Tree and SVM. The system also maintains low false positive and false negative rates of 2.1% and 1.8%, respectively. Additionally, explainable AI techniques are integrated to provide transparency in decision-making, while privacy-preserving methods ensure compliance with data protection standards.
References
[1] F. Siddiqui and M. Suaib, “Enhancing Spammer Fake Profile Detection on Social Media Platforms using Artificial Neural Networks,” International Journal of Engineering and Management Research, vol.13, no.4, pp.1-6, 2023.
[2] G. Jethava and U. P. Rao, “Exploring security and trust mechanisms in online social networks: An extensive review,” Computers & Security, vol.140, no.103790, 2024.
[3] F. Siddiqui and M. Suaib, “Machine learning approaches for fake user and spammer detection: A comprehensive review and future perspectives,” International Journal of Engineering and Management Research, vol.13, no.3, pp.23-46, 2023.
[4] K. Smita, N. Harika, N. Advaitha, O. L. Kalyani and T. Kruthika, “Fake Profile Identification in Social Network Using Machine Learning and NLP,” Turkish Journal of Computer and Mathematics Education, vol.14, no.3, pp.689-699, 2023.
[5] S. Verma and S. Shukla, “Review of Fake Profile Classification and Identification on Social Networks,” Grenze International Journal of Engineering & Technology (GIJET), vol.10, 2024.
[6] N. A. Nawaz, K. Ishaq, U. Farooq, A. Khalil, S. Rasheed, A. Abid and F. Rosdi, “A comprehensive review of security threats and solutions for the online social networks industry,” PeerJ Computer Science, vol.9, no.e1143, 2023.
[7] A. K. Tyagi, K. Naithani and S. Tiwari, “Security and Possible Threats in Today's Online Social Networking Platforms,” Online Social Networks in Business Frameworks, pp.159-199, 2024.
[8] A. Mughaid, I. Obeidat, S. AlZu’bi, E. A. Elsoud, A. Alnajjar, A. R. Alsoud and L. Abualigah, “A novel machine learning and face recognition technique for fake accounts detection system on cyber social networks,” Multimedia Tools and Applications, vol.82, no.17, pp.26353-26378, 2023.
[9] S. Y. Modadugu, S. S. N. Rao and D. V. Reddy, “Extreme learning machine for spammer detection and fake user identification from twitter,” In 2024 IEEE 13th International Conference on Communication Systems and Network Technologies (CSNT). IEEE, pp.1141-1146, 2024.
[10] M. J. Alam, I. Hossain, S. Puppala and S. Talukder, “Combating identity attacks in online social networks: A multi-layered framework using zero-knowledge proof and permissioned blockchain,” In Proceedings of the International Conference on Advances in Social Networks Analysis and Mining, pp.636-643, 2023.
[11] S. P. Jayanna, S. Venkateswarlu, B. I. Bharathi, C. H. Mahitha, P. Praharshitha and K. Nikhitha, “Fake Social Media Profile Detection And Reporting,” Metallurgical and Materials Engineering, pp.965-971, 2025.
[12] A. Shah, S. Varshney and M. Mehrotra, “Threats on online social network platforms: classification, detection, and prevention techniques,” Multimedia Tools and Applications, pp.1-33, 2024.
[13] Sreedhhar Bhukya, K. VinayKumar and N. C. Santosh, “A Novel Dynamic Novel Growth model for Mobile Social Networks,” Journal of Computer Allied Intelligence, vol.2, no.1, pp.46-53, 2024.
[14] Pratap Rajkumar Lalwani and Varsha S. Sukhadeve, “Impact of Technology 4.0 on Quality Service, Customer Satisfaction Pertaining to E-Commerce Platforms,” International Journal of Advance Research and Innovation, vol.12, no.1, pp.1-6, 2024.
[15] T. Bikku, N. S. Biyyapu, J. C. Sekhar, M. K. Kumar, S. M. Nokerov and V. K. Pratap, “The Social Network Dilemma: Safeguarding Privacy and Security in an Online Community,” International Journal of Safety & Security Engineering, vol.14, no.1, 2024.
[16] E. Alnagi, A. Ahmad, Q. A. Al-Haija and A. Aref, “Unmasking Fake Social Network Accounts with Explainable Intelligence,” International Journal of Advanced Computer Science & Applications, vol.15, no.3, 2024.
[17] Rekha Gangula and Rega Sravani, “Enhanced Detection of Social Bots on Online Platforms using SemiSupervised K-Means Clustering,” Journal of Sensors, IoT & Health Sciences, vol.2, no.1, pp.6- 16, 2024.
[18] D. Javed, N. Z. Jhanjhi, N. A. Khan, S. K. Ray, A. Al-Dhaqm and V. R. Kebande, “Identification of Spambots and Fake Followers on Social Network via Interpretable AI-Based Machine Learning,” IEEE Access, 2025.
[19] A. T. Mahmood, R. K. Naser and S. K. Abd, “The Security System in Information Technology Detects Fake Accounts on the Youtube Platform,” International Journal of Intelligent Engineering & Systems, vol.16, no.1, 2023.
[20] L. Caruccio, G. Cimino, S. Cirillo, D. Desiato, G. Polese and G. Tortora, “Malicious account identification in social network platforms,” ACM Transactions on Internet Technology, vol.23, no.4, pp.1-25, 2023.
[21] M. Bhattacharya, S. Roy, S. Chattopadhyay, A. K. Das and S. Shetty, “A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges,” Security and Privacy, vol.6, no.1, pp.e275, 2023.
[22] W. Dracewicz and M. Sepczuk, “Detecting Fake Accounts on Social Media Portals—the X Portal Case Study,” Electronics, vol.13, no.13, pp.2542, 2024.
[23] A. Podorozhniak, N. Liubchenko, V. Oliinyk and V. Roh, “Research application of the spam filtering and spammer detection algorithms on social media and messengers,” Advanced Information Systems, vol.7, no.3, pp.60-66, 2023.
[24] R. Chauhan, A. Singh, C. Bhatt and H. U. Habiba, “Spammer detection and fake user detection using modified Naïve Bayes and decision Tree classifier,” In 2023 Second International Conference on Informatics (ICI). IEEE, pp.1-5, 2023.
[25] I. Haq, W. Qiu, J. Guo and T. Peng, “Spammy names detection in pashto language to prevent fake accounts creation on social media,” In 2023 8th International Conference on Signal and Image Processing (ICSIP). IEEE, pp.614-618, 2023.
[26] D. R. Patil, T. M. Pattewar, V. D. Punjabi and S. M. Pardeshi, “Detecting Fake Social Media Profiles Using the Majority Voting Approach,” EAI Endorsed Transactions on Scalable Information Systems, vol.11, no.3, 2024.
