Abstract:
This study presents a comprehensive analysis of information diffusion in social
networks with time delay mechanisms. We first analyze real Reddit thread data, identifying
limitations in the sample size. To overcome this, we develop synthetic network models
with varied structural properties. Our approach tests three delay types (constant, uniform,
exponential) across different network structures, using machine learning models to identify
key factors influencing information coverage. The results show that spread probability
consistently impacts diffusion across all datasets. Gradient Boosting models achieve
R
2 = 0.847 on synthetic data. Random networks with a constant delay mechanism and high
spread probability (0.4) maximize coverage. When verified against test data, peak speed
time emerges as the strongest predictor (r = 0.995, p < 0.001). Our findings provide practical
recommendations for optimizing information spread in social networks and demonstrate
the value of integrating real and synthetic data in diffusion modeling.
