  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.
