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Understanding political news media consumption with digital trace data and natural language processing

Author

Listed:
  • Ruben L. Bach
  • Christoph Kern
  • Denis Bonnay
  • Luc Kalaora

Abstract

Augmenting survey data with digital traces is a promising direction for combining the advantages of active and passive data collection. However, extracting interpretable measurements from digital traces for social science research is challenging. In this study, we demonstrate how to obtain measurements of news media consumption from survey respondents' web browsing data using Bidirectional Encoder Representations from Transformers, a powerful natural language processing algorithm that estimates contextual word embeddings from text data. Our approach is particularly relevant for political scientists and communication researchers studying exposure to online news content but can easily be adapted to projects in other disciplines working with similar data sets.

Suggested Citation

  • Ruben L. Bach & Christoph Kern & Denis Bonnay & Luc Kalaora, 2022. "Understanding political news media consumption with digital trace data and natural language processing," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 246-269, December.
    • Handle: RePEc:bla:jorssa:v:185:y:2022:i:s2:p:s246-s269
    DOI: 10.1111/rssa.12846
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    References listed on IDEAS

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    1. Ruben L Bach & Alexander Wenz, 2020. "Studying health-related internet and mobile device use using web logs and smartphone records," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-20, June.
    2. Michael Scharkow & Frank Mangold & Sebastian Stier & Johannes Breuer, 2020. "How social network sites and other online intermediaries increase exposure to news," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(6), pages 2761-2763, February.
    3. Andrew M. Guess & Brendan Nyhan & Jason Reifler, 2020. "Exposure to untrustworthy websites in the 2016 US election," Nature Human Behaviour, Nature, vol. 4(5), pages 472-480, May.
    4. Matthew Gentzkow & Jesse M. Shapiro, 2011. "Ideological Segregation Online and Offline," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 126(4), pages 1799-1839.
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    Cited by:

    1. Patrick Oliver Schenk & Christoph Kern, 2024. "Connecting algorithmic fairness to quality dimensions in machine learning in official statistics and survey production," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 18(2), pages 131-184, June.

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