The key element of Facebook social network platform is the status updates, in which the user can upload text or other media such as pictures and videos. In this study, we manually classified more than 3500 Facebook status updates (FSUs) by the subject, the emotional activation, the medium used, the originality and the self-centeredness. We then cross-tabulated that information with demographic factors such as gender and occupation. Thirty students participated in the categorization task, each annotating more than 100 FSUs of their Facebook friends’ FSUs. Statistical and supervised machine learning analysis was then applied to the categorized features. The text itself was not analyzed further after the annotation for the purpose of preserving the privacy and anonymity of the FSU authors. Results show that FSUs vary in subject, emotional connotation and structure as a function of demographic factors like gender and occupation of the poster. Statistical analysis and supervised machine learning are able to predict the demographic and emotional expressions based on the other features annotated by the participants.