Individual behavior and social influence in online social systems

Abstract

The capacity to collect and analyze the actions of individuals in online social systems at minute-by-minute time granularity offers new perspectives on collective human behavior research. Macroscopic analysis of massive datasets raises interesting observations of patterns in online social processes. But working at a large scale has its own limitations, since it typically doesn't allow for interpretations on a microscopic level. We examine how different types of individual behavior affect the decisions of friends in a network. We begin with the problem of detecting social influence in a social system. Then we investigate the causality between individual behavior and social influence by observing the diffusion of an innovation among social peers. Are more active users more influential? Are more credible users more influential? Bridging this gap and finding points where the macroscopic and microscopic worlds converge contributes to better interpretations of the mechanisms of spreading of ideas and behaviors in networks and offer design opportunities for online social systems.