ICMR 2014, 1st International Workshop on Social Multimedia and Storytelling (SoMuS 2014), CEUR Proceedings, Volume 1198, April 1, 2014, Glasgow, Scotland
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 les 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
di erent 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 nally comment on the stories we obtained and we provide statistics that give meaningful insights for improving the system.
Type:
Conference
City:
Glasgow
Date:
2014-04-01
Department:
Data Science
Eurecom Ref:
4244
Copyright:
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