Extracting Resources that Help Tell Events' Stories

Social media platforms constitute a valuable source of information regarding real-world happenings. In particular, user generated content on mobile-oriented platforms like Twitter allows for real-time narrations thanks to the instantaneous nature of publishing. A common practice for users is to include in the tweets links pointing to articles, media files and other resources. In this paper, we are interested in how the resources shared in a stream of tweets for an event can be analyzed, and how can they help tell the event story. We describe a system that extracts, resolves, and eventually filters the resources shared in tweets content according to two different ranking functions. We are interested in how these two ranking functions perform (with respect to speed and accuracy) for discovering important and relevant resources that will tell the event story. We describe an experiment on a sample set of events where we evaluate those functions. We finally comment on the stories we obtained and we provide statistics that give meaningful insights for improving the system.