Machine-learning media bias.
Journal
PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081
Informations de publication
Date de publication:
2022
2022
Historique:
received:
21
01
2022
accepted:
10
07
2022
entrez:
10
8
2022
pubmed:
11
8
2022
medline:
13
8
2022
Statut:
epublish
Résumé
We present an automated method for measuring media bias. Inferring which newspaper published a given article, based only on the frequencies with which it uses different phrases, leads to a conditional probability distribution whose analysis lets us automatically map newspapers and phrases into a bias space. By analyzing roughly a million articles from roughly a hundred newspapers for bias in dozens of news topics, our method maps newspapers into a two-dimensional bias landscape that agrees well with previous bias classifications based on human judgement. One dimension can be interpreted as traditional left-right bias, the other as establishment bias. This means that although news bias is inherently political, its measurement need not be.
Identifiants
pubmed: 35947584
doi: 10.1371/journal.pone.0271947
pii: PONE-D-22-02110
pmc: PMC9365193
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e0271947Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
Références
Nat Methods. 2020 Mar;17(3):261-272
pubmed: 32015543