Social networks need to manage and control the drift of huge amounts of information by filtering and summarizing everything, in order to ensure they satisfy users' viewing pleasure. Until now social media content has already been used in a variety of applications such as for ranking of news stories, for profiling of user preferences, even for products' recommendations. However, this type of conversational, user-generated content might be used to add value to more traditional event media, such as video. In this paper we examine the capability of automatically producing meaningful summaries of generic videos. To do so we consider EdgeRank's affinity, weight and time decay parameters and implement a CLARANS-based key-frames extraction scheme. This paper forms an initial study of a social media video abstraction service and experiments indicate its promising performance.