Machine-Learning media bias

TL;DR

This paper introduces an automated approach to measure media bias by analyzing phrase frequencies in articles, mapping newspapers into a two-dimensional bias space that aligns with traditional political bias classifications.

Contribution

The paper presents a novel automated method for quantifying media bias using phrase frequency analysis, providing a bias landscape consistent with human judgments.

Findings

The method maps newspapers into a two-dimensional bias space.

One dimension correlates with traditional left-right bias.

The other dimension reflects establishment bias.

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.