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 bia...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0271947 |
| _version_ | 1818495168597196800 |
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| author | Samantha D'Alonzo Max Tegmark |
| author_facet | Samantha D'Alonzo Max Tegmark |
| author_sort | Samantha D'Alonzo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T18:16:48Z |
| format | Article |
| id | doaj.art-d5f8b500956b4a1092f8f284b7aaf54f |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-10T18:16:48Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-d5f8b500956b4a1092f8f284b7aaf54f2022-12-22T01:38:19ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01178e027194710.1371/journal.pone.0271947Machine-learning media bias.Samantha D'AlonzoMax TegmarkWe 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.https://doi.org/10.1371/journal.pone.0271947 |
| spellingShingle | Samantha D'Alonzo Max Tegmark Machine-learning media bias. PLoS ONE |
| title | Machine-learning media bias. |
| title_full | Machine-learning media bias. |
| title_fullStr | Machine-learning media bias. |
| title_full_unstemmed | Machine-learning media bias. |
| title_short | Machine-learning media bias. |
| title_sort | machine learning media bias |
| url | https://doi.org/10.1371/journal.pone.0271947 |
| work_keys_str_mv | AT samanthadalonzo machinelearningmediabias AT maxtegmark machinelearningmediabias |