Social platforms open a window to what is happening in the world in near real-time: (micro-)posts and media items are shared by people to report their feelings and their activities related to any type of events. Such an information can be collected and analyzed in order to get the big picture of an event from the crowd point of view. In this paper, we present a general framework to capture and analyze micro-posts containing media items relevant to a search term. We describe the results of an experiment that consists in collecting fresh social media posts (posts containing media items) from numerous social platforms in order to generate the story of the “2013 Italian Election”. Items are grouped in meaningful time intervals that are further analyzed through deduplication, clusterization, and visual representation. The final output is a storyboard that provides a satirical summary of the elections as perceived by the crowd. A screencast showing an example of these functionalities is published at http://youtu.be/jIMdnwMoWnk while the system is publicly available at http://mediafinder.eurecom.fr/story/elezioni2013 .