Automating News Content Analysis: An Application to Gender Bias and Readability
omar ali, Ilias Flaounas, Tijl De Bie, Nick Mosdell, Justin Lewis and Nello Cristianini
In this article we present an application of text-analysis technologies to support social science research, in particular the analysis of patterns in news content. We describe a system that gathers and annotates large volumes of textual data in order to extract patterns and trends. We have examined 3.5 million news articles and show that their topic is related to the gender bias and readability of their content. This study is intended to illustrate how pattern analysis technology can be deployed to automate tasks commonly performed by humans in the social sciences, in order to enable large scale studies that would otherwise be impossible.