  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.
