Abstract
In the contemporary digital landscape, the pervasive and far-reaching impact of online social networks is indisputable. Prominent platforms such as Instagram, Facebook, and Twitter frequently grapple with the persistent challenge of distinguishing between registered profiles and genuinely engaged users, resulting in a noticeable surge in the prevalence of counterfeit or dormant accounts. This situation underscores the compelling necessity to accurately differentiate between authentic and misleading user profiles. The primary objective of this investigation is to introduce an innovative approach to profile validation. This unique method astutely leverages state-of-the-art bio-inspired algorithms while circumventing traditional machine learning techniques. The empirical results are notably convincing, consistently achieving a high level of accuracy in classification tests conducted on the provided datasets.
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Mahammed, N., Klouche, B., Saidi, I., Khaldi, M., Fahsi, M. (2024). Enhancing Social Media Profile Authenticity Detection: A Bio-Inspired Algorithm Approach. In: Renault, É., Boumerdassi, S., Mühlethaler, P. (eds) Machine Learning for Networking. MLN 2023. Lecture Notes in Computer Science, vol 14525. Springer, Cham. https://doi.org/10.1007/978-3-031-59933-0_3
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