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Related Experiment Video

Updated: May 28, 2026

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
08:53

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

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
PubMed
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.

Keywords:
branching processdata-driven modelinginformation propagationsocial media

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Last Updated: May 28, 2026

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Published on: May 31, 2019

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.