Coverage of the Dutch nitrogen crisis in Dutch newspapers between 2019 and 2024

Abstract

The news media play an important role to assist citizens in becoming informed about crises, which allows them to evaluate the causes and responsibilities. When publishing news, news media can utilise frames to influence the perceptions and impressions of their audiences. Previous research has manually analysed the used frames regarding the coverage of the Dutch nitrogen crisis, but did so for a short period of time or did not cover the differences between the analysed outlets. Therefore, journalistic practices, regarding this crisis, have not yet been thoroughly analysed, which shows a gap in the research. The current research tries to fill this gap by analysing longitudinal shifts regarding the coverage of the Dutch nitrogen crisis. The aim of this study is to determine if computational methods can be used to determine imbalances and biases in framing practices, in order to monitor journalistic practices. To research this, 9,374 articles from June 2019 until March 2024 were extracted and analysed using Natural Language Processing techniques. Topic modelling and Named Entity Recognition were applied to perform a framing analysis on the dataset, which were visualised using knowledge graphs. The results show shifts regarding the topics and entities, with the topics being quite diverse and the entities being mostly framed politically. Lastly, the knowledge graphs showed to be successful in capturing the context of the topics and entities, while also indicating a difference between the frames that outlets applied. The results show that these methods can be used to develop a tool for analysing journalistic practices. Development of this tool should start after addressing the limitations, while the current study did not implement sentiment regarding the topics and only allowed for one topic per article. Additionally, it would be good practice to extend the case beyond the nitrogen crisis, to validate the findings of this study. The developed tool can support news organisations in monitoring imbalances and biases regarding their framing, supporting them in critically analysing their reporting styles.