Machine-learning media bias
Name
journal.pone.0271947.pdf
Description
Published version
Size
3.8 MB
Format
Adobe PDF
Checksum (MD5)
9bb8e21a7c66312f31d4bcc5835c8843
Author(s)
D’Alonzo, Samantha
Tegmark, Max
Date Issued
2022
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
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.
MIT Department
Massachusetts Institute of Technology. Department of Physics
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1371/journal.pone.0271947
