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.
@article{arxiv.2109.00024,
title = {Machine-Learning media bias},
author = {Samantha D'Alonzo and Max Tegmark},
journal= {arXiv preprint arXiv:2109.00024},
year = {2022}
}
Comments
29 pages, 23 figs; data available at https://space.mit.edu/home/tegmark/phrasebias.html