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dc.contributor.advisorKrøvel, Roy
dc.contributor.authorLønrusten, Kajsa Garmann
dc.date.accessioned2023-09-26T10:32:21Z
dc.date.available2023-09-26T10:32:21Z
dc.date.issued2023
dc.identifier.urihttps://hdl.handle.net/11250/3092005
dc.description.abstractThis thesis has examined a dataset of Norwegian climate articles in an attempt at answering the research questions: What are the main drivers in Norwegian climate journalism, what challenges does this bring to light concerning Norwegian climate journalism, and how can interdisciplinary collaboration and AI facilitate as methods in this research? The dataset of climate articles in the time period 1st of January 2021 to 31st of May 2022, was examined by using quantitative programming analysis and supplemented with a quali-quantitative survey of Norwegian journalists. AI was additionally utilised as a tool through an interdisciplinary collaboration with Faktisk.no and OsloMet. The findings of this thesis demonstrates how valuable interdisciplinary collaboration can be and how AI can be applied as a research method in such a project. Furthermore, the main drivers in Norwegian climate journalism have been found to be major political and mainly international climate-centred events such as implementation of new climate policies, the release of the IPCC’s climate reports, and climate summits. These findings display the discontinuity in Norwegian climate coverage, and the many challenges that journalists face when covering climate change. The challenges have been found to be rooted in the complexity of the issue, while the need for more resources, time and expertise on the issue within the newsroom is evident.en_US
dc.language.isoengen_US
dc.publisherOsloMet-Storbyuniversiteteten_US
dc.subjectClimate journalismen_US
dc.subjectKlimajournalistikken_US
dc.subjectClimate changeen_US
dc.subjectArtificial Intellegenceen_US
dc.titleThe Uneven Climate of Norwegian Climate Journalism: A study of the drivers in Norwegian climate journalism and its challenges, using quantitative programming methods and exploring interdisciplinarityen_US
dc.typeMaster thesisen_US
dc.description.versionpublishedVersionen_US


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