Cybersecurity on social media platforms: The use of deep learning methodologies for detecting anomalous user behavior

The exponential growth of social media platforms has brought unprecedented opportunities for global communication, networking, and information exchange. However, this expansion has also given rise to significant challenges, particularly in identifying and mitigating anomalous or malicious user behaviors such as spamming, cyberbullying, and misinformation dissemination. Traditional anomaly detection methods, which often rely on rule-based systems or basic statistical models, have proven inadequate in addressing the complex, dynamic, and evolving nature of user behavior on these platforms. In response, this paper explores the application of deep learning methodologies—specifically Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), Autoencoders, and Generative Adversarial Networks (GANs)—for detecting anomalous behavior in social media environments. We present a novel deep learning architecture that integrates these models to enhance the detection accuracy of behavioral anomalies. By leveraging large-scale datasets and advanced feature extraction techniques, our approach demonstrates significant improvements over traditional methods, with higher precision, recall, and overall detection rates. The proposed model's ability to adapt to new and emerging patterns of behavior underscores its potential for real-world application in monitoring social media platforms. This research contributes to the growing body of literature on deep learning for cybersecurity and digital trust, offering a robust solution for maintaining the integrity of online social spaces. Our findings suggest that the implementation of these advanced methodologies can provide a more secure and reliable social media environment, benefiting both users and platform providers alike.