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.
One of the oldest references on mining television content is a DARPA-sponsored workshop in 1999 with a topic detection and tracking challenge @cite_10 . Higher-level applications have been emerging in recent years. For example, describe a system for finding web pages related to television content, and test different methods to synthesize a web search query from a television transcript. classify videos based on a transcription obtained from speech recognition. describe a system to rate the credibility of information items on television by looking at how often the same image is described in a similar way by more than one news source.
@cite_10
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This paper describes the creation and content of the TDT-2 corpus in the context of the TDT-2 research project it supports and in comparison to previous and subsequent efforts