Cold-Start Fake Account Detection on Instagram Using Deep Learning: A Systematic Review of Methods and Gaps - Mwanazaire, Lydia; Kamau, John (International Journal of Computing and Engineering, 2025)
Purpose: The primary objective of this systematic review is to evaluate the effectiveness of Deep Learning (DL) architectures in detecting "cold-start" fake accounts on Instagram newly created profiles that lack sufficient historical data for traditional detection. Methodology: The methodology focused on five core DL frameworks Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Generative Adversarial Networks (GANs), and Autoencoders evaluating their ability to process non-textual features, metadata, and early-stage behavioral patterns. Findings: The findings reveal that hybrid models, specifically those combining GANs for data augmentation with LSTMs for sequence analysis, achieve the highest detection accuracy of up to 96.4% for cold-start profiles. However, a significant transparency-accuracy trade-off persists, as ensemble methods often lack the interpretability required for platform-wide implementation and struggle to distinguish between "quiet" legitimate users and sophisticated "human-impersonating" bots during the critical first 48 hours of account activity. Unique Contribution to Theory, Policy and Practice: This study contributes to theory by introducing an integrated framework for "digital identity evolution" that moves beyond static feature analysis toward dynamic, behavior-based detection. In practice, it provides platform developers with a technical roadmap for implementing hybrid-optimization models, such as combining DL with bio-inspired algorithms like GWO and PSO, to reduce false-positive rates. Finally, for policy, the research offers evidence-based recommendations for regulatory frameworks regarding social media transparency, asserting that early detection is essential to mitigate the 1.3 billion annual economic loss caused by influencer fraud and advertising waste.