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Machine-learning media bias

Author

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  • Samantha D’Alonzo
  • Max Tegmark

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.

Suggested Citation

  • Samantha D’Alonzo & Max Tegmark, 2022. "Machine-learning media bias," PLOS ONE, Public Library of Science, vol. 17(8), pages 1-24, August.
    • Handle: RePEc:plo:pone00:0271947
    DOI: 10.1371/journal.pone.0271947
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    References listed on IDEAS

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    1. Matthew Gentzkow & Jesse M. Shapiro, 2010. "What Drives Media Slant? Evidence From U.S. Daily Newspapers," Econometrica, Econometric Society, vol. 78(1), pages 35-71, January.
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