When no news is bad news: Detection of negative events from news media content

TY - ABST

T1 - When no news is bad news: Detection of negative events from news media content

AU - Baglini, Rebekah Brita

AU - Nielbo, Kristoffer Laigaard

AU - Hæstrup, Frida

AU - Enevoldsen, Kenneth

AU - Vahlstrup, Peter Bjerregaard

AU - Roepstorff, Andreas

N1 - Conference code: 2021

PY - 2021/6/2

Y1 - 2021/6/2

N2 - 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.

AB - 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.

KW - NLP

KW - Digital humanities

KW - news media

UR - https://2021.dhbenelux.org/home/abstracts/#nielbo

M3 - Conference abstract for conference

T2 - Digital Humanities Benelux

Y2 - 2 June 2021 through 4 June 2021

ER -