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Characterizing Information Propagation in Social Media with Branching Processes
Xiaofang Luo1, Haibo Hu1, Qingsong Sun2
1Department of Management Science and Engineering, East China University of Science and Technology, Shanghai 200237, China.
Entropy (Basel, Switzerland)
|May 26, 2026
Summary
This study uses branching processes to model information diffusion on social media, validating the approach with real-world data. The findings confirm branching processes accurately replicate information cascade characteristics.
Area of Science:
- Social Network Analysis
- Computational Social Science
- Information Science
Background:
- Information propagation in social media is a significant area of research.
- Branching processes, common in biology, are being explored for modeling information diffusion.
- Data-driven studies applying branching processes to social media are scarce.
Purpose of the Study:
- To characterize and model social media information diffusion using branching processes.
- To validate the reliability of branching models with empirical data.
- To advance data-driven models for information spreading in complex systems.
Main Methods:
- Utilized empirical data from social media cascades.
- Applied branching process methodology for modeling.
- Compared theoretical predictions, numerical simulations, and empirical results.
Main Results:
- The branching model accurately replicates key statistical characteristics of real-world information cascades.
- The model's reliability was verified against empirical data and simulations.
- Demonstrated the effectiveness of branching processes in capturing diffusion dynamics.
Conclusions:
- Branching processes are a valid and reliable tool for modeling information diffusion on social media.
- This research contributes to developing more sophisticated, data-driven models for information spread.
- The study validates the applicability of theoretical biology methods in social science contexts.
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