title: How random are online social interactions?
abstract: The massive amounts of data that social media generates has facilitated the study of online human behavior on a scale unimaginable a few years ago. At the same time, the much discussed apparent randomness with which people interact online makes it appear as if these studies cannot reveal predictive social behaviors that could be used for developing better platforms and services. We use two large social databases to measure the mutual information entropy that both individual and group actions generate as they evolve over time. We show that user's interaction sequences have strong deterministic components, in contrast with existing assumptions and models. In addition, we show that individual interactions are more predictable when users act on their own rather than when attending group activities.
authors.affiliation: Department of Applied Physics, Stanford University, California, USA.
authors.forename: Chunyan
authors.initials: C
authors.lastname: Wang
authors.forename: Bernardo A
authors.initials: BA
authors.lastname: Huberman
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D006801:Humans; D007257:Information Theory; D016013:Likelihood Functions; D008954:Models, Biological; D017711:Nonlinear Dynamics; D012919:Social Behavior; D061108:Social Media