| Status: Finished | First online: 11-02-2021 | Updated: NA |
Authors Kristoffer L. Nielbo [1,2,3], Frida Haestrup, Kenneth C. Enevoldsen [1], Peter B. Vahlstrup [1,2], Rebekah B. Baglini [3], and Andreas Roepstorff [3].
[1] Center for Humanities Computing Aarhus, Jens Chr. Skous Vej 4, Building 1483, 3rd floor, DK-8000 Aarhus C, Denmark
[1] DATALAB, School of Communication and Culture, Aarhus University, Helsingforsgade 14, DK-8200 Aarhus N, Denmark
[1] Interacting Minds Centre, Jens Chr. Skous Vej 4, Building 1483, 3rd floor, DK-8000 Aarhus C, Denmark
Important: this is an pre-print; it should not be relied upon without context to guide clinical practice or health-related behavior and should not be reported in news media as established information without consulting multiple experts in the field.
Abstract
During the first wave of Covid-19 information decoupling could be observed in the flow of news media content. The corollary of the content alignment within and between news sources experienced by readers (i.e., all news transformed into Corona-news), was that the novelty of news content went down as media focused monotonically on the pandemic event. This all-important Covid-19 news theme turned out to be quite persistent as the pandemic continued, resulting in the, from a news media’s perspective, paradoxical situation where the same news was repeated over and over. This information phenomenon, where novelty decreases and persistence increases, has previously been used to track change in news media, but in this study we specifically test the claim that new information decoupling behavior of media can be used to reliably detect change in news media content originating in a negative event, using a Bayesian approach to change point detection.
Keywords
Newspapers, Pandemic Response, Bayesian Change Detection, Information Theory
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