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

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  • Oleg S. Nagornyy

    () (National Research University Higher School of Economics)

  • Olessia Y. Koltsova

    () (National Research University Higher School of Economics)

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

Suggested Citation

  • Oleg S. Nagornyy & Olessia Y. Koltsova, 2017. "Mining Media Topics Perceived as Social Problems by Online Audiences: Use of a Data Mining Approach in Sociology," HSE Working papers WP BRP 74/SOC/2017, National Research University Higher School of Economics.
  • Handle: RePEc:hig:wpaper:74/soc/2017
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    File URL: https://wp.hse.ru/data/2017/05/15/1171306878/74SOC2017.pdf
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    References listed on IDEAS

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    1. Mike Thelwall & Kevan Buckley & Georgios Paltoglou & Di Cai & Arvid Kappas, 2010. "Sentiment strength detection in short informal text," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(12), pages 2544-2558, December.
    2. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(03), pages 267-297, June.
    3. Mike Thelwall & Kevan Buckley & Georgios Paltoglou, 2011. "Sentiment in Twitter events," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(2), pages 406-418, February.
    4. Kevin Wallsten, 2007. "Agenda Setting and the Blogosphere: An Analysis of the Relationship between Mainstream Media and Political Blogs," Review of Policy Research, Policy Studies Organization, vol. 24(6), pages 567-587, November.
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    More about this item

    Keywords

    social problem; online media; topic modeling; sentiment analysis; Russia;

    JEL classification:

    • Z - Other Special Topics

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