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

Omar Ali, Ilias Flaounas, Tijl De Bie, Nick Mosdell, Justin Lewis, Nello Cristianini
Proceedings of the First Workshop on Applications of Pattern Analysis, PMLR 11:36-43, 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v11-ali10a, title = {Automating News Content Analysis: An Application to Gender Bias and Readability}, author = {Ali, Omar and Flaounas, Ilias and Bie, Tijl De and Mosdell, Nick and Lewis, Justin and Cristianini, Nello}, booktitle = {Proceedings of the First Workshop on Applications of Pattern Analysis}, pages = {36--43}, year = {2010}, editor = {Diethe, Tom and Cristianini, Nello and Shawe-Taylor, John}, volume = {11}, series = {Proceedings of Machine Learning Research}, address = {Cumberland Lodge, Windsor, UK}, month = {01--03 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v11/ali10a/ali10a.pdf}, url = {https://proceedings.mlr.press/v11/ali10a.html}, 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.} }
Endnote
%0 Conference Paper %T Automating News Content Analysis: An Application to Gender Bias and Readability %A Omar Ali %A Ilias Flaounas %A Tijl De Bie %A Nick Mosdell %A Justin Lewis %A Nello Cristianini %B Proceedings of the First Workshop on Applications of Pattern Analysis %C Proceedings of Machine Learning Research %D 2010 %E Tom Diethe %E Nello Cristianini %E John Shawe-Taylor %F pmlr-v11-ali10a %I PMLR %P 36--43 %U https://proceedings.mlr.press/v11/ali10a.html %V 11 %X 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.
RIS
TY - CPAPER TI - Automating News Content Analysis: An Application to Gender Bias and Readability AU - Omar Ali AU - Ilias Flaounas AU - Tijl De Bie AU - Nick Mosdell AU - Justin Lewis AU - Nello Cristianini BT - Proceedings of the First Workshop on Applications of Pattern Analysis DA - 2010/09/30 ED - Tom Diethe ED - Nello Cristianini ED - John Shawe-Taylor ID - pmlr-v11-ali10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 11 SP - 36 EP - 43 L1 - http://proceedings.mlr.press/v11/ali10a/ali10a.pdf UR - https://proceedings.mlr.press/v11/ali10a.html AB - 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. ER -
APA
Ali, O., Flaounas, I., Bie, T.D., Mosdell, N., Lewis, J. & Cristianini, N.. (2010). Automating News Content Analysis: An Application to Gender Bias and Readability. Proceedings of the First Workshop on Applications of Pattern Analysis, in Proceedings of Machine Learning Research 11:36-43 Available from https://proceedings.mlr.press/v11/ali10a.html.

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