Says who? Automatic Text-Based Content Analysis of Television News

TL;DR

This paper presents an automated approach to analyze television news content using closed captions, revealing insights on linguistic style, mentions of people, and biases across numerous US channels over six months.

Contribution

It introduces a comprehensive method for automatic analysis of TV news content, including segmentation, annotation, and bias detection using NLP techniques.

Findings

Identified patterns in news provider coverage

Detected biases in news reporting

Analyzed linguistic styles across channels

Abstract

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.