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.
['Ovidiu Dan', 'Junlan Feng', 'Brian D. Davison']
Filtering microblogging messages for social tv