MMM 2014, 20th International Conference on MultiMedia Modeling, 8-10th January 2014, Dublin, Ireland / Also published in LNCS, Volume 8326/2014
As the amount of social media shared on the Internet grows increasingly, it becomes possible to explore a topic with a novel, people based viewpoint. We aim at performing topic enriching using media items mined from social media sharing platforms. Nevertheless, such data
collected from the Web is likely to contain noise, hence the need to further process collected documents to ensure relevance. To this end, we designed an approach to automatically propose a cleaned set of media items related to events mined from search trends. Events are described
using word tags and a pool of videos is linked to each event in order to propose relevant content. This pool has previously been ltered out from non-relevant data using information retrieval techniques. We report the results of our approach by automatically illustrating the popular moments of four celebrities.
Type:
Conférence
City:
Dublin
Date:
2014-01-08
Department:
Data Science
Eurecom Ref:
4154
Copyright:
© Springer. Personal use of this material is permitted. The definitive version of this paper was published in MMM 2014, 20th International Conference on MultiMedia Modeling, 8-10th January 2014, Dublin, Ireland / Also published in LNCS, Volume 8326/2014 and is available at : http://dx.doi.org/10.1007/978-3-319-04117-9_24
