Framing a Conflict! How Media Report on Earthquake Risks Caused by Gas Drilling: A Longitudinal Analysis Using Machine Learning Techniques of Media Reporting on Gas Drilling from 1990 to 2015
Using a new analytical tool, supervised machine learning (SML), a large number of newspaper articles is analysed to answer the question how newspapers frame the news of public risks, in this case of earthquakes caused by gas drilling in The Netherlands. SML enabled the study of 2265 news articles published over a period of 25 years. Our study shows that there is a disproportional relation between media reporting and actual risk; and that the use of dramatization bias in framing the issue about gas drilling increased, but the use of personalization and negativity bias did not become more dominant after a major media change in 2013. Sensational/tabloid newspapers make more use of personalization bias, whereas quality newspapers make more use of value conflict and political disagreement in the framing about gas drilling.
|Keywords||framing, machine learning technique, media bias, mediatization, risk, risk amplification|
|Persistent URL||dx.doi.org/10.1080/1461670X.2017.1418672, hdl.handle.net/1765/104361|
Opperhuizen, A.E. (Alette Eva), Schouten, K, & Klijn, E-H. (2018). Framing a Conflict! How Media Report on Earthquake Risks Caused by Gas Drilling: A Longitudinal Analysis Using Machine Learning Techniques of Media Reporting on Gas Drilling from 1990 to 2015. Journalism Studies, 1–21. doi:10.1080/1461670X.2017.1418672