How Random are Online Social Interactions?

TL;DR

This paper analyzes online social interactions using mutual information entropy, revealing that individual and group behaviors are more deterministic and predictable than previously assumed, especially when users act independently.

Contribution

It demonstrates that online social interactions contain strong deterministic patterns, challenging the notion of randomness and providing insights into predictability of user behavior.

Findings

Interaction sequences have strong deterministic components.

Individual actions are more predictable than group activities.

Users acting alone exhibit higher predictability.

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.