PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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
JMLR W&CP Volume 11, pp. 36-43, 2010.

Abstract

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.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:9220
Deposited By:Ilias Flaounas
Deposited On:21 February 2012