Détection automatique des faux comptes sur les réseaux sociaux à l’aide de l’apprentissage profond

Abstract

In the digital age, social media platforms have become essential tools for communication, information dissemination, and brand visibility. However, this widespread use has given rise to a growing concern: the proliferation of fake accounts, particularly on Instagram. These inauthentic profiles, often automated or maliciously crafted, pose serious threats to user security, distort engagement metrics, and serve as vehicles for disinformation and fraudulent activities. To address this challenge, this thesis presents a deep learning-based approach using Long Short-Term Memory (LSTM) neural networks, which are well-suited to modeling the sequential and behavioral data of social media users. A synthetic dataset representing Instagram accounts was used to train and evaluate the model. The results highlight the method’s ability to accurately classify accounts as genuine or fake, offering strong performance metrics and promising generalization capabilities. This research contributes to the broader field of cybersecurity and illustrates the potential of artificial intelligence in detecting online threats and enhancing digital platform integrity