A Robust Prediction of Online Social Network Influencers by Using Elevated Deep Learning Strategy

Predicting influential users is crucial for targeted marketing, knowledge diffusion, and community participation on dynamic social networks. We offer a deep learning-based methodology to predict social network influencers in this research. Using a deep learning framework to integrate user behavior, interaction patterns, and network structure identifies influential users with top accuracy. In this paper, the Social Media Influence Principle (SMIP) is developed using state-of-the-art neural network architectures like Graph Neural Network (GNN) to capture relational dynamics and temporal aspects of user interaction. This method's cross-validation with Recurrent Neural Network (RNN), which considers multi-layer relations and temporal information inside a sequence of social media network snapshots, is better than any previous effort. NEMO can identify community battleground nodes using engagement analytics, sentiments analysis, and network centrality. The present research advances social network analysis by providing a reliable influencer prediction tool that informs data-driven digital marketing/environment management decisions.