The Majority Illusion in Social Networks

TL;DR

The paper investigates the 'majority illusion' in social networks, where local perceptions are skewed by network structure, influencing social contagion and perceptions, and provides models to quantify this effect.

Contribution

It identifies the origin of the majority illusion linked to the friendship paradox and develops a statistical model to measure its impact in various networks.

Findings

The majority illusion can significantly distort perceptions of behavior prevalence.

Network structure critically influences the strength of the majority illusion.

The model accurately predicts the magnitude of the illusion in synthetic and real-world networks.

Abstract

Social behaviors are often contagious, spreading through a population as individuals imitate the decisions and choices of others. A variety of global phenomena, from innovation adoption to the emergence of social norms and political movements, arise as a result of people following a simple local rule, such as copy what others are doing. However, individuals often lack global knowledge of the behaviors of others and must estimate them from the observations of their friends' behaviors. In some cases, the structure of the underlying social network can dramatically skew an individual's local observations, making a behavior appear far more common locally than it is globally. We trace the origins of this phenomenon, which we call "the majority illusion," to the friendship paradox in social networks. As a result of this paradox, a behavior that is globally rare may be systematically overrepresented in the local neighborhoods of many people, i.e., among their friends. Thus, the "majority illusion" may facilitate the spread of social contagions in networks and also explain why systematic biases in social perceptions, for example, of risky behavior, arise. Using synthetic and real-world networks, we explore how the "majority illusion" depends on network structure and develop a statistical model to calculate its magnitude in a network.