Anomalous structure and dynamics in news diffusion among heterogeneous individuals

Area of Science:

• Network Science
• Information Diffusion
• Computational Social Science

Background:

• Previous studies identified network hubs (well-connected nodes) as primary information spreaders.
• The role of ordinary users (less-connected nodes) in information diffusion remains less understood.

Purpose of the Study:

• To investigate the distinct roles of nodes with varying connection counts in online news sharing.
• To identify conditions under which ordinary users can outperform network hubs in information dissemination.

Main Methods:

• Quantitative analysis of online news sharing behavior.
• Examination of diffusion patterns and distance distributions.
• Development and application of a message-passing model.

Main Results:

• Ordinary users can spread news more effectively than hubs when diffusion follows dendrite-like paths, creating non-Gaussian distance distributions.
• Hub-dominated diffusion typically results in Gaussian distance distributions.
• Increased interactions among ordinary users are crucial for non-Gaussian diffusion characteristics.

Conclusions:

• Information diffusion dynamics depend significantly on the interplay between hubs and ordinary users.
• Network structure and user interaction patterns determine whether hubs or ordinary users dominate information spread.
• The average number of forwards can distinguish between hub-dominated and ordinary user-dominated diffusion patterns.