Opinion Dynamics and Social Network Analysis

The field of opinion dynamics and social network analysis is moving towards a deeper understanding of how information cascades and social interactions influence public discourse. Recent research has focused on developing new models and frameworks to study the spread of information and opinions on online social networks, highlighting the importance of considering the interplay between social dynamics and information flow. A key area of innovation is the development of novel methods for inferring diffusion networks and identifying causal effects in complex systems. Noteworthy papers in this area include:

  • The proposal of the Friedkin-Johnsen on Cascade model, which integrates information cascades and opinion dynamics.
  • The development of a novel double mixture directed graph model for inferring multi-layer diffusion networks from cascade data.
  • The presentation of the first upper bound on the convergence time to consensus of the $h$-majority dynamics with $k$ opinions, providing new insights into the dynamics of opinion formation.

Sources

Cascade-driven opinion dynamics on social networks

How online misinformation works: a costly signalling perspective

Inferring Diffusion Structures of Heterogeneous Network Cascade

Identifying Macro Causal Effects in C-DMGs over DMGs

Collaborative governance of cyber violence: A two-phase, multi-scenario four-party evolutionary game and SBI1I2R public opinion dissemination

On the $h$-majority dynamics with many opinions

Opinion Dynamics with Highly Oscillating Opinions

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