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

Bibliographic Data

ID21377327
AuthorsAlette Eva Opperhuizen (Erasmus University Rotterdam, corresponding author), Kim Schouten (0000-0003-1604-0333, Erasmus University Rotterdam)
Year2021
Volume55
Issue1
Pages19-37
Publication date2021-02-01
Open AccessYes
TypeARTICLE
VenueQuality & Quantity (JOURNAL)
Journal identifiersISSN: 0033-5177 • E-ISSN: 1573-7845
PublisherSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s11135-020-00992-w
OpenAlexW3026199030
LanguageEN
Citations received3
References cited41

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

Big data · Cognitive reframing · Content analysis · Data mining · Data science · Media studies · Newspaper · Point (geometry) · Qualitative research · Sentence · Social science · Sociology · Thematic analysis · Tipping point (physics) · Artificial Intelligence · Computational and Text Analysis Methods · Computer Science · Engineering · Mathematics · Psychology · Public Relations and Crisis Communication · Risk Perception and Management · Social Psychology

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