The access to Online Social Networks (OSN) and to media shared over these platforms account for around 20% of today's mobile Internet traffic. For mobile device users, the access to media content and specifically videos is still challenging and costly. Mobile contracts usually have a data cap and connection qualities can vary greatly, depending on the cellular network coverage. Prefetching mechanisms that fetch content items beforehand, in times when the mobile device is connected to a WiFi network, have a high potential to address these problems. Yet, such a mechanism can only be effective if relevant content can be predicted with a high accuracy. Therefore, in this paper, an analysis of content properties and their potential for prediction are presented. An initial user study with 14 Facebook users running an app on their mobile device was conducted. The results show that video consumption is very diverse across the users. This work discusses the evaluation setup, the data analysis, and their potential to define an effective prefetching algorithm.