An Extended Firefly Algorithm for Enhanced Information Diffusion with Multi-Factor Considerations

Understanding and predicting how information spreads in online social networks is a crucial yet complex task, especially with the growing influence of content type, user engagement, and social dynamics. In this study, we propose an enhanced information diffusion model based on an Extended Modified Firefly Algorithm (EMFA), integrating four key features: content type, engagement level, temporal behavior, and user social attributes. Unlike classical models, which treat information diffusion uniformly, our approach adapts dynamically to the nature of content and the behavioral patterns of users. The proposed model is evaluated using multiple real-world datasets, including Twitter and Reddit, and compared against state-of-the-art optimization-based diffusion models such as PSO, ACO, and GWO. The experimental results show that incorporating these factors significantly improves the accuracy and realism of diffusion predictions. We also conducted a sensitivity analysis to assess the individual impact of each factor and demonstrated the model’s robustness in simulating viral trends and predicting peak diffusion times. This work contributes a refined and adaptive computational framework for simulating complex diffusion dynamics in modern social ecosystems and opens pathways for applications in rumor control, digital marketing, and social behavior forecasting.