Model of Opinion Spreading in Social Networks

TL;DR

This paper introduces a new opinion spreading model emphasizing the '0-1-2 effect', where exposure to two opinionated neighbors significantly increases adoption probability, highlighting the influence of network topology on spreading dynamics.

Contribution

The paper presents a novel opinion spreading model that captures the differential impact of neighbor exposure and demonstrates the importance of network structure and the '0-1-2 effect' in opinion dynamics.

Findings

Small world networks significantly affect tipping point timing.

The '0-1-2' effect greatly influences opinion adoption probabilities.

Network topology determines influencer impact in opinion spreading.

Abstract

We proposed a new model, which capture the main difference between information and opinion spreading. In information spreading additional exposure to certain information has a small effect. Contrary, when an actor is exposed to 2 opinioned actors the probability to adopt the opinion is significant higher than in the case of contact with one such actor (called by J. Kleinberg "the 0-1-2 effect"). In each time step if an actor does not have an opinion, we randomly choose 2 his network neighbors. If one of them has an opinion, the actor adopts opinion with some low probability, if two - with a higher probability. Opinion spreading was simulated on different real world social networks and similar random scale-free networks. The results show that small world structure has a crucial impact on tipping point time. The "0-1-2" effect causes a significant difference between ability of the actors to start opinion spreading. Actor is an influencer according to his topological position in the network.