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.
The closed captions are streams of plain text that we process through a series of steps. First, to segment the text stream into sentences we use a series of heuristics which include detecting a change of speaker, conventionally signaled by a text marker ( @math ''), using the presence of full stops, and using time-based rules. We remark that there exist methods to join sentences into passages @cite_6 @cite_8 , but for our analysis we use single sentences as basic units of content, and we only group them when they match to the same news item, as described in .
@cite_6 @cite_8
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Large volumes of information in video format are being created and made available from a number of application areas, including movies, broadcast TV, CCTV, education video materials, and so on. As this information is increasingly in digital format, this creates the opportunity and then the demand for content-based access to such material. One particular kind of video information that we are interested in is broadcast TV news and in this paper we report on our work on developing content-based access to broadcast TV news. Our work is carried out within the context of the Fischlar system, developed to allow content access to large volumes of digital video information. We report our work on Fischlar-News which provides text search based on closed caption information as well as our on-going work on segmenting TV News programmes and providing personalised intelligent access to TV news stories, on fixed as well as mobile platforms. In this paper, we introduce and evaluate two novel approaches, one using video stream and the other using close-caption text stream, for segmenting TV news into stories. The segmentation of the video stream into stories is achieved by detecting anchor person shots and the text stream is segmented into stories using a Latent Dirichlet Allocation (LDA) based approach. The benefit of the proposed LDA based approach is that along with the story segmentation it also provides the topic distribution associated with each segment. We evaluated our techniques on the TRECVid 2003 benchmark database and found that though the individual systems give comparable results, a combination of the outputs of the two systems gives a significant improvement over the performance of the individual systems.