Automating News Content Analysis: An Application to Gender Bias and Readability

Omar Ali, Ilias Flaounas, Tijl De Bie, Nick Mosdell, Justin Lewis, Nello Cristianini

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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.
Translated title of the contributionAutomating News Content Analysis: An Application to Gender Bias and Readability
Original languageEnglish
Title of host publicationProceedings of the First Workshop on Applications of Pattern Analysis (WAPA 2010)
Subtitle of host publication1-3 September 2010, Cumberland Lodge, Windsor, UK
PublisherMassachusetts Institute of Technology (MIT) Press
Pages36-43
Number of pages8
Publication statusPublished - Sep 2010

Publication series

NameJMLR Workshop and Conference Proceedings
PublisherJMLR
Volume11
ISSN (Print)1938-7228

Bibliographical note

ISBN: 19387228
Publisher: JMLR: Workshop and Conference Proceedings
Name and Venue of Conference: Workshop on Applications of Pattern Analysis (WAPA)
Other identifier: 2001261

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