The enormous rise in the generation of content on the Web, has made it increasingly important to automatically organize and understand this information. With the unprecedented success of on-line social network platforms, such as Facebook, Twitter and Flickr, users have changed their roles from being only information consumers, to also becoming editors and publishers. In particular, Twitter as become a preferred source for breaking news to an important percentage of Web users. The streaming nature of this platform, facilitates real-time information dissemination, before its publication in traditional mass media. Moreover, not only objective facts regarding news are hared through social media, but also multiple opinions and points of view. Therefore, social media activity surrounding news events provides rich insight into human perception of world events. Nevertheless, the rapid flow of large data volumes in Twitter makes information volatile, producing the loss of potentially important historical data for users and for society in general as well. Following this motivation, in this presentation I will talk about the potential of user-generated content and social media data for modeling, summarizing and retrieving news events. I will present our current work in the direction of understanding events in social media as complex units of information, using sentiment, visualization and geo-temporal context.