Mining Media Topics Perceived as Social Problems by Online Audiences: Use of a Data Mining Approach in Sociology

22 Pages Posted: 15 May 2017 Last revised: 17 May 2017

See all articles by Oleg Nagornyy

Oleg Nagornyy

National Research University Higher School of Economics

Olessia Koltsova

National Research University Higher School of Economics

Date Written: May 15, 2017

Abstract

Media audiences that represent a significant part of a county’s public may hold opinions on media-generated definitions of social problems different from those of media professionals. The proliferation of user-generated content makes such opinions available, but simultaneously demands new automatic methods of analysis that media scholars still have to master. In this paper, we show how topics regarded as problematic by media consumers may be revealed and analyzed by social scientists with a combination of data mining methods. Our dataset consists of 33,877 news items and 258,121 comments from a sample of regional newspapers. With a number of new, but simple indices we find that issue salience in media texts and its popularity with audience diverge. We conclude that our approach can help communication scholars effectively detect both popular and negatively perceived topics as good proxies of social problems.

Keywords: social problem, online media, topic modeling, sentiment analysis, Russia

JEL Classification: Z

Suggested Citation

Nagornyy, Oleg and Koltsova, Olessia, Mining Media Topics Perceived as Social Problems by Online Audiences: Use of a Data Mining Approach in Sociology (May 15, 2017). Higher School of Economics Research Paper No. WP BRP 74/SOC/2017. Available at SSRN: https://ssrn.com/abstract=2968359 or http://dx.doi.org/10.2139/ssrn.2968359

Oleg Nagornyy (Contact Author)

National Research University Higher School of Economics ( email )

Myasnitskaya street, 20
Moscow, Moscow 119017
Russia

Olessia Koltsova

National Research University Higher School of Economics ( email )

Myasnitskaya street, 20
Moscow, Moscow 119017
Russia

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