Machine-Learning media bias
This provides an automated tool for researchers and analysts to quantify media bias across topics, though it is incremental as it builds on existing bias classification concepts.
The authors tackled the problem of measuring media bias by developing an automated method that analyzes phrase frequencies in articles to map newspapers into a two-dimensional bias space, achieving good agreement with human-based classifications.
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.