Competing social contagions with opinion-dependent infectivity.

Area of Science:

• Social network analysis
• Computational social science
• Information dynamics

Background:

• Disinformation, or maliciously spread false information, proliferates on online social networks.
• Cognitive biases, such as confirmation bias and the mere-exposure effect, predispose individuals to accept repeated or belief-aligned information.
• Disinformation often competes directly with true information for adoption within a population.

Purpose of the Study:

• To develop and analyze a model for the spread of two competing beliefs on a social network.
• To investigate the role of individual cognitive biases in modulating belief adoption.
• To examine how initial conditions and external recruitment affect long-term belief dynamics.

Main Methods:

• Agent-based modeling to simulate individual interactions and belief adoption.
• Mean-field description to analyze the macroscopic dynamics of belief spreading.
• Numerical simulations to explore various initial conditions and external recruitment strategies.

Main Results:

• Cognitive biases significantly enrich the transient dynamics of belief spreading.
• Biases can lead to phenomena such as the revival of a previously declining belief.
• The model demonstrates the potential for beliefs to overturn initially dominant opinions.
• External recruitment of spreaders can determine the eventual dominance of one belief over another.

Conclusions:

• Cognitive biases are critical factors in understanding the complex dynamics of information and disinformation spread.
• The interplay between individual biases and network structure influences belief propagation.
• Strategic external recruitment can be employed to control the outcome of competing belief dynamics.