Predictive Modeling of Opinion and Connectivity Dynamics in Social Networks

TL;DR

This paper introduces a dynamic agent-based social network model that captures opinion and connectivity changes over time, validated with real-world data, and useful for studying opinion spread and network evolution strategies.

Contribution

The paper presents a novel dynamic agent-based model that reflects real-world social network changes and enables analysis of opinion propagation and structural evolution.

Findings

Model accurately predicts opinion spread and network connectivity changes.

Network parameters significantly influence opinion dynamics.

The approach provides insights into social network evolution.

Abstract

Recent years saw an increased interest in modeling and understanding the mechanisms of opinion and innovation spread through human networks. Using analysis of real-world social data, researchers are able to gain a better understanding of the dynamics of social networks and subsequently model the changes in such networks over time. We developed a social network model that both utilizes an agent-based approach with a dynamic update of opinions and connections between agents and reflects opinion propagation and structural changes over time as observed in real-world data. We validate the model using data from the Social Evolution dataset of the MIT Human Dynamics Lab describing changes in friendships and health self-perception in a targeted student population over a nine-month period. We demonstrate the effectiveness of the approach by predicting changes in both opinion spread and connectivity of the network. We also use the model to evaluate how the network parameters, such as the level of `openness' and willingness to incorporate opinions of neighboring agents, affect the outcome. The model not only provides insight into the dynamics of ever changing social networks, but also presents a tool with which one can investigate opinion propagation strategies for networks of various structures and opinion distributions.