Modeling Facebook Posting Life-Cycle

As Facebook becomes a quite relevant tool for companies marketing and sales it is important to analyze and understand posting activity. Propagation of relevant episodes in Facebook is quite fast and companies must not only plan, monitor and control the posting activities in their own Facebook page but also understand what is happening in their competitors Facebook. This paper presents a model and algorithm that allows the implementation of automated monitoring of Facebook posting activity, identifying normal and outliers in their activity, and hence enhancing companies' Facebook competitive intelligence. The model is validated with a data sample of 27924 public publications from the 550 companies Facebook pages.