Diffusion dynamics of competing information on networks.

Area of Science:

• Social Network Analysis
• Information Diffusion Dynamics
• Computational Social Science

Background:

• Existing threshold models for information diffusion on social networks do not account for individual optimization problems.
• The dynamics of information propagation when individuals face multiple, potentially conflicting, information sources remain an open question.

Purpose of the Study:

• To develop a microfounded general threshold model for analyzing collective dynamics in the propagation of multiple information sources.
• To investigate how individual optimization problems influence information diffusion on social networks.

Main Methods:

• Development of a microfounded general threshold model.
• Analysis of collective dynamics under different individual decision-making assumptions (coordination with neighbors, irreversible choices).

Main Results:

• The virality of competing information sources is fundamentally indeterminate.
• Maximizing coordination with neighbors leads to a saddle path diffusion process, resulting in unpredictable symmetry breaking.
• Irreversible individual choices create a continuum of stable equilibria, leading to chance-driven social polarization.

Conclusions:

• Individual optimization is critical for understanding complex information diffusion patterns on social networks.
• The indeterminacy of competing information spread highlights the unpredictable nature of social dynamics.
• Social polarization can emerge as a probabilistic outcome of individual decision-making in information consumption.