document: Filtering microblogging messages for social tv Social TV was named one of the ten most important emerging technologies in 2010 by the MIT Technology Review . Manufacturers of set-top boxes and televisions have recently started to integrate access to social networks into their products . Some of these systems allow users to read microblogging messages related to the TV program they are currently watching . However , such systems suffer from low precision and recall when they use the title of the show as keywords when retrieving messages , without any additional filtering . We propose a bootstrapping approach to collecting microblogging messages related to a given TV program . We start with a small set of annotated data , in which , for a given show and a candidate message , we annotate the pair to be relevant or irrelevant . From this annotated data set , we train an initial classifier . The features are designed to capture the association between the TV program and the message . Using our initial classifier and a large dataset of unlabeled messages we derive broader features for a second classifier to further improve precision .
doc_bio_tags: B B O O B I B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O