A large scale agent-based model of common Facebook users was designed to develop an understanding of the underlying mechanism of information diffusion within online social networks at a micro-level analysis. The agent-based model network structure is based on a sample from Facebook. Using an erased configuration model and the idea of common neighbours, a new correction procedure was investigated to overcome the problem of missing graph edges to construct a representative sample of the Facebook network graph. The model parameters are based on assumptions and general activity patterns (such as posting rate, time spent on Facebook etc.) taken from general data on Facebook. Using the agent-based model, the impact of post length, post score and publisher's friend count on the spread of wall posts in several scenarios was analyzed. Findings indicated that post content has the highest impact on the success of post propagation. However, amusing and absorbing but lengthy posts (e.g. a funny video) do not spread as well as short but unremarkable ones (e.g. an interesting photo). In contrast to product adoption and disease spread propagation models, the absence of a similar "epidemic" threshold in Facebook post diffusion is observed.