Cascade-driven opinion dynamics on social networks

Abstract

Online social networks (OSNs) have transformed the way individuals fulfill their social needs and consume information. As OSNs become increasingly prominent sources for news dissemination, individuals often encounter content that influences their opinions through both direct interactions and broader network dynamics. In this paper, we propose the Friedkin-Johnsen on Cascade (FJC) model, which is, to the best of our knowledge, is the first attempt to integrate information cascades and opinion dynamics, specifically using the very popular Friedkin-Johnsen model. Our model, validated over real social cascades, highlights how the convergence of socialization and sharing news on these platforms can disrupt opinion evolution dynamics typically observed in offline settings. Our findings demonstrate that these cascades can amplify the influence of central opinion leaders, making them more resistant to divergent viewpoints, even when challenged by a critical mass of dissenting opinions. This research underscores the importance of understanding the interplay between social dynamics and information flow in shaping public discourse in the digital age.

Figures (8)

• Figure 1: States, transitions and actions of the FJC model for a single node. The blue boxes indicate the states and the orange arrows with corresponding probabilities indicate the transitions between states. The purple arrows and boxes indicate the actions made within the corresponding states.
• Figure 2: A toy example of a network in (a) and the corresponding propagation tree for $r=1$ in (b). Nodes $2$ and $3$ update their opinion deterministically, while the others do it with probability $\theta$.
• Figure 3: ECDF of the opinion shift of nodes, i.e. final opinion minus initial opinion, for the Nepal top cascades in the three different opinion dynamics. (A) $\lambda_i \sim \mathrm{P}_i$, (B) $\lambda_i \sim \mathrm{P}_i^{-1}$, (C) $\lambda_i = 0.6$.
• Figure 4: Polarization value of the different vectors in the corresponding metrics in the three different opinion dynamics for Nepal top cascades. (A) $\lambda_i \sim \mathrm{P}_i$, (B) $\lambda_i \sim \mathrm{P}_i^{-1}$, (C) $\lambda_i = 0.6$.
• Figure 5: Polarizing opinions vs. node centrality with $\theta = 0.01$. Each arrow represents a node, starting at its initial opinion (prejudice) and ending at its final opinion under the FJC model. Arrow color indicates node susceptibility; black dots show the final opinions under the FJ model. (A) $\lambda_i \propto \mathrm{P}_i$; (B) $\lambda_i \propto \mathrm{P}_i^{-1}$; (C) $\lambda_i = 0.6$. Dot-dashed and dashed lines show the average initial and final FJC opinions.

Theorems & Definitions (2)

• Theorem 1
• Definition 1