Audience Behavior Mining by Integrating TV Ratings with Multimedia Contents

TV ratings are a widely used indicator in the TV broadcasting field. While TV ratings are mainly used in advertising, they can also be used as a social sensor that reflects the interests of people. This paper presents a framework for discovering audience behavior through the mining of TV ratings. We have established a framework that enables discovery of numerous patterns of audience behavior from TV ratings. Used along with other multimedia contents such as video and text, it enables various types of knowledge to be semi-automatically found, such as what types of news programs are of most interest and what are the key visual features for acquiring high TV ratings. The discovery of audience behavior is achieved by focusing on the change points in the rating data, i.e., the points in time where many people switch the channel or turn the television on or off. Rich descriptions that characterize these points are extracted from multimedia contents, and then various filtering techniques are used to extract specific patterns of interest. Several applications of this framework for discovering knowledge demonstrated that it can effectively extract various types of audience behavior. To the best of our knowledge, this work is the first work to analyze the use of ratings data in combination with video and other multimedia data.