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Dynamics and tipping point of issue attention in newspapers: quantitative and qualitative content analysis at sentence level in a longitudinal study using supervised machine learning and big data

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  • A. E. Opperhuizen

    (Erasmus School of Social and Behavioural Sciences
    Erasmus University Rotterdam)

  • K. Schouten

    (Erasmus School of Social and Behavioural Sciences
    Erasmus University Rotterdam)

Abstract

This study aims to provide a more sensitive understanding of the dynamics and tipping points of issue attention in news media by combining the strengths of quantitative and qualitative research. The topic of this 25-year longitudinal study is the volume and the content of newspaper articles about the emerging risk of gas drilling in The Netherlands. We applied supervised machine learning (SML) because this allowed us to study changes in the quantitative use of subtopics at the detailed sentence level in a large number of articles. The study shows that the actual risk of drilling-induced seismicity gradually increased and that the volume of newspaper attention for the issue also gradually increased for two decades. The sub-topics extracted from media articles during the low media attention period, covering factual information, can be interpreted as a part of episodic frame patterns about the drilling and its consequences. However, a sudden major shift in newspaper attention can be observed in 2013. This sudden disjointed expansion in the volume of media attention on this large-scale technology occurred after a governmental authority classified the drilling-induced earthquakes as a safety issue. After the disjointed issue expansion, safety and decision making were the main subtopics linked to the thematic frames, responsibility, conflict, human interest, and morality. We conclude that SML is a promising tool for future analysis of the growing number of publicly available digitalized textual big datasets, particularly for longitudinal studies and analysis of tipping points and reframing.

Suggested Citation

  • A. E. Opperhuizen & K. Schouten, 2021. "Dynamics and tipping point of issue attention in newspapers: quantitative and qualitative content analysis at sentence level in a longitudinal study using supervised machine learning and big data," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(1), pages 19-37, February.
    • Handle: RePEc:spr:qualqt:v:55:y:2021:i:1:d:10.1007_s11135-020-00992-w
    DOI: 10.1007/s11135-020-00992-w
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    References listed on IDEAS

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    1. Charles Vlek, 2018. "Induced Earthquakes from Long‐Term Gas Extraction in Groningen, the Netherlands: Statistical Analysis and Prognosis for Acceptable‐Risk Regulation," Risk Analysis, John Wiley & Sons, vol. 38(7), pages 1455-1473, July.
    2. Jamie K. Wardman & Ragnar Löfstedt, 2018. "Anticipating or Accommodating to Public Concern? Risk Amplification and the Politics of Precaution Reexamined," Risk Analysis, John Wiley & Sons, vol. 38(9), pages 1802-1819, September.
    3. Michael Scharkow, 2013. "Thematic content analysis using supervised machine learning: An empirical evaluation using German online news," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(2), pages 761-773, February.
    4. Ines Lörcher & Irene Neverla, 2015. "The Dynamics of Issue Attention in Online Communication on Climate Change," Media and Communication, Cogitatio Press, vol. 3(1), pages 17-33.
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    1. Chih-Hsing Liu & Jeou-Shyan Horng & Sheng-Fang Chou & Tai-Yi Yu & Yung-Chuan Huang & Jun-You Lin, 2023. "Integrating big data and marketing concepts into tourism, hospitality operations and strategy development," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(2), pages 1905-1922, April.

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