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
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

e0271947

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Références

Nat Methods. 2020 Mar;17(3):261-272
pubmed: 32015543

Auteurs

Samantha D'Alonzo (S)

Dept. of Physics and Institute for AI & Fundamental Interactions, Massachusetts Institute of Technology, Cambridge, MA, United States of America.

Max Tegmark (M)

Dept. of Physics and Institute for AI & Fundamental Interactions, Massachusetts Institute of Technology, Cambridge, MA, United States of America.

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Classifications MeSH