Machine-learning media bias.

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  • Author(s): D'Alonzo S;D'Alonzo S; Tegmark M; Tegmark M
  • Source:
    PloS one [PLoS One] 2022 Aug 10; Vol. 17 (8), pp. e0271947. Date of Electronic Publication: 2022 Aug 10 (Print Publication: 2022).
  • Publication Type:
    Journal Article
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
    • Publication Information:
      Original Publication: San Francisco, CA : Public Library of Science
    • Subject Terms:
    • Abstract:
      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.
      Competing Interests: The authors have declared that no competing interests exist.
    • References:
      Nat Methods. 2020 Mar;17(3):261-272. (PMID: 32015543)
    • Publication Date:
      Date Created: 20220810 Date Completed: 20220812 Latest Revision: 20220826
    • Publication Date:
      20240105
    • Accession Number:
      PMC9365193
    • Accession Number:
      10.1371/journal.pone.0271947
    • Accession Number:
      35947584