Prediction of Influential Spreaders in Complex Social Networks Using Hybrid Sarimax-LSTM Model

In the digital era, the rapid and widespread diffusion of information through social media has made it essential to identify influential spreaders—users who can significantly impact information propagation. This study proposes a hybrid predictive model that combines Seasonal Autoregressive Integrated Moving Average with exogenous factors (SARIMAX) and Long Short-Term Memory (LSTM) networks to forecast influence trends within complex social networks. The model leverages both statistical time-series forecasting and deep learning to capture linear seasonal patterns and complex non-linear behaviours in user interactions. Through systematic data preprocessing, including noise removal, tokenization, and lemmatization, the model processes structured and unstructured data to identify key influencers. Evaluation results demonstrate high precision, recall, and F1-scores, confirming the model’s effectiveness in dynamic environments. The proposed system offers valuable applications in marketing, crisis management, and public opinion tracking, supporting real-time influencer identification with enhanced accuracy and robustness.