The short message service Twitter has gained significant popularity and uptake among participants of conferences and organized events as a backchannel for intra-event communication. Information that is exchanged explicitly through such tweets, or that is implicitly present in them, remains mostly hidden and undecipherable to machines. In this paper we propose a framework for extracting valuable information from conference tweets, enabling its publication as Linked Data. We introduce the concept of mapping tweets with the talks and subevents that they refer to, in doing so gaining access to additional information about the users, talks and dynamics of the event. We present preliminary results of our work towards tweet-talk mappings and motivate our current and future work by giving several use cases for such extracted data.