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Max Tegmark: Machine-Learning media bias (arxiv.org)
4 points by Anon84 on Oct 9, 2021 | hide | past | pdf | discuss on HN

In plain words: By guessing which newspaper wrote an article from the phrases it uses, the method places papers and their wording on a two-dimensional bias map. On about a million articles from a hundred papers, it matched human left-right ratings and revealed a second establishment dimension.

Abstract · Machine-Learning media bias

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.

Samantha D'Alonzo, Max Tegmark
arXiv:2109.00024 · cs.CY, cs.CL, cs.LG · submitted Aug 31, 2021
abstract · pdf · html · 29 pages, 23 figs; data available at https://space.mit.edu/home/tegmark/phrasebias.html

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