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Branching dynamics of viral information spreading
José Luis Iribarren1, Esteban Moro
1Instituto de Ingeniería del Conocimiento, Universidad Autónoma de Madrid, E-28049 Madrid, Spain. jose.iribarren@iic.uam.es
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|December 21, 2011
Summary
Information spreading dynamics, crucial for marketing and social networks, are better understood with a new branching process model. This model accounts for human behavior variability and predicts viral diffusion tipping points.
Area of Science:
- Social Network Analysis
- Information Dynamics
- Computational Social Science
Background:
- The dynamics of information spreading are complex and not fully explained by traditional epidemic models.
- Human behavior heterogeneity significantly influences diffusion patterns in peer-to-peer networks.
- Understanding viral propagation is key for marketing, social networking, and innovation dissemination.
Purpose of the Study:
- To analyze the structure and dynamics of information diffusion in a large-scale real-world viral marketing campaign.
- To develop a novel model that accurately describes information spreading, incorporating human behavioral variability.
- To provide a framework for predicting and managing viral information propagation.
Main Methods:
- Tracking the spread of a controlled message across a diffusion graph of over 31,000 individuals.
- Analyzing the non-Markovian branching dynamics of information propagation.
- Modeling the diffusion process using a two-step Bellman-Harris branching process.
Main Results:
- Information spreading exhibits non-Markovian branching dynamics, deviating from standard contagion models.
- A two-step Bellman-Harris branching process accurately models the observed information diffusion.
- The model successfully explains features of information propagation, including "tipping point" phenomena.
Conclusions:
- The generalized branching process offers a more accurate representation of viral information spreading than traditional models.
- This model can be applied to predict and manage the spread of information in social and marketing contexts.
- Accounting for human behavioral variability is essential for understanding and controlling information diffusion.
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