Says who? Automatic Text-Based Content Analysis of Television News
This work addresses the need for scalable analysis of media bias and content in television news, though it is incremental as it applies existing NLP methods to a new dataset.
The researchers tackled the problem of analyzing television news content by automatically processing closed captions from over 140 US channels over six months, using NLP methods to uncover insights about linguistic style, people mentioned, and biases, with results including qualitative assessments from multiple perspectives.
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.