Opinion Dynamics of Online Social Network Users: A Micro-Level Analysis

TL;DR

This empirical study analyzes how online social network users' opinions evolve through friendship influences, revealing that opinion shifts depend on the divergence between users and their friends, with shifts being more likely at moderate divergence levels.

Contribution

The paper provides a large-scale empirical analysis of opinion dynamics, distinguishing positive and negative shifts and modeling their relation to opinion divergence.

Findings

Opinion shifts are positively related to opinion divergence.

Positive shifts are less likely when opinions are too similar or too dissimilar.

Opinion divergence influences the balance of positive and negative opinion shifts.

Abstract

In this paper, we present an empirical study of the opinion dynamics of a large-scale sample of online social network users. We estimate opinions of users as continuous scalars based on their subscriptions to information sources and analyze how friendship connections affect the dynamics of these estimations. Distinguishing between positive (toward opinions of friends) and negative (away from opinions of friends) opinion shifts, we find that the existence and magnitude of opinion shifts are positively related (largely through a linear form or an inverted U-shaped form) to the degree of divergence in opinions between user and their friends. Additionally, we moderate the balance between positive and negative shifts using opinion divergence: if the opinions of the focal user and their friends are too similar or dissimilar, there is a relatively low chance of a positive shift.