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Machine-learning media bias

Author(s)
D’Alonzo, Samantha; Tegmark, Max
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Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/
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Abstract
<jats:p>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.</jats:p>
Date issued
2022
URI
https://hdl.handle.net/1721.1/144452
Department
Massachusetts Institute of Technology. Department of Physics
Journal
PLOS ONE
Publisher
Public Library of Science (PLoS)
Citation
D’Alonzo, Samantha and Tegmark, Max. 2022. "Machine-learning media bias." PLOS ONE, 17 (8).
Version: Final published version

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