Machine-Learning media bias

• URL: http://arxiv.org/abs/2109.00024v1
• Date: Tue, 31 Aug 2021 18:06:32 GMT
• Title: Machine-Learning media bias
• Authors: Samantha D'Alonzo (MIT), Max Tegmark (MIT)
• Abstract summary: Inferring which newspaper published a given article leads to a conditional probability distribution whose analysis lets us automatically map newspapers 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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