Complex Contagion Features without Social Reinforcement in a Model of Social Information Flow.

Area of Science:

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

Background:

• Simple contagion models fail to capture real-world information spread complexities.
• Complex contagion models incorporate social reinforcement, where multiple exposures are needed for sharing.
• Observed phenomena include the weakness of long ties and density-dependent inhibition of information flow.

Purpose of the Study:

• To investigate if the quoter model, a model for written information flow, exhibits complex contagion characteristics.
• To determine if social reinforcement is a necessary mechanism for complex contagion behaviors in networks.
• To explore the interplay between network structure and information spreading dynamics.

Main Methods:

• Utilizing the quoter model to simulate information flow over social networks.
• Analyzing model outputs for features characteristic of complex contagion, such as tie strength and network density effects.
• Comparing quoter model dynamics to established complex contagion models.

Main Results:

• The quoter model demonstrates complex contagion features, including the weakness of long ties.
• Increased network density was observed to inhibit, rather than promote, information flow within the quoter model.
• These complex contagion-like behaviors emerged without an explicit social reinforcement mechanism.

Conclusions:

• The quoter model exhibits complex contagion properties without explicit social reinforcement.
• Network properties significantly influence information flow, independent of direct reinforcement mechanisms.
• An information-theoretic perspective is crucial for understanding social behavior and network effects on information spread.