We perform an automatic analysis of television news programs, based on the closed captions that accompany them. Specifically, we collect all the news broadcasted in over 140 television channels in the US during a period of six months. We start by segmenting, processing, and annotating the closed captions automatically. Next, we focus on the analysis of their linguistic style and on mentions of people using NLP methods. We present a series of key insights about news providers, people in the news, and we discuss the biases that can be uncovered by automatic means. These insights are contrasted by looking at the data from multiple points of view, including qualitative assessment.
IntoNow is a mobile application that provides a second-screen experience to television viewers. IntoNow uses the microphone of the companion device to sample the audio coming from the TV set, and compares it against a database of TV shows in order to identify the program being watched. The system we demonstrate is activated by IntoNow for specific types of shows. It retrieves information related to the program the user is watching by using closed captions, which are provided by each broadcasting network along the TV signal. It then matches the stream of closed captions in real-time against multiple sources of content. More specifically, during news programs it displays links to online news articles and the profiles of people and organizations in the news, and during music shows it displays links to songs. The matching models are machine-learned from editorial judgments, and tuned to achieve approximately 90 precision.