TVPulse: detecting TV highlights in Social Networks

Abstract

Sharing live experiences in social networks is a growing trend. That includes posting comments and sentiments about TV programs. Automatic detection of messages with contents related to TV allows a numerous quantity of applications in the industry of entertainment information. This paper describes a system that is capable of detecting TV highlights in one of the most important social networks - Twitter. Combining Twitter’s messages and information from an Electronic Programming Guide (EPG) we built a model that matches tweets with TV programs with an accuracy over 80%. Our model required the construction of semantic profiles for the Portuguese language. These semantic profiles are used to identify the most representative tweets as highlights of a TV program. Far from finished, we intend to further develop our system to take advantage of external metadata in order to improve matching rates.