Whether they are modeling bookmarking behavior in Flickr or cascades of failure in large networks, models of diffusion often start with the assumption that a few nodes start long chain reactions, resulting in large-scale cascades. While rea-sonable under some conditions, this assumption may not hold for social media networks, where user engagement is high and information may enter a system from multiple dis-connected sources.

Using a dataset of 262,985 Facebook Pages and their as-sociated fans, this paper provides an empirical investigation of diffusion through a large social media network. Although Facebook diffusion chains are often extremely long (chains of up to 82 levels have been observed), they are not usually the result of a single chain-reaction event. Rather, these dif-fusion chains are typically started by a substantial number of users. Large clusters emerge when hundreds or even thousands of short diffusion chains merge together.

This paper presents an analysis of these diffusion chains using zero-inflated negative binomial regressions. We show that after controlling for distribution effects, there is no meaningful evidence that a start node’s maximum diffusion chain length can be predicted with the user’s demographics or Facebook usage characteristics (including the user’s number of Facebook friends). This may provide insight into future research on public opinion formation.