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dc.contributor.authorD’Alonzo, Samantha
dc.contributor.authorTegmark, Max
dc.date.accessioned2022-08-26T12:46:37Z
dc.date.available2022-08-26T12:46:37Z
dc.date.issued2022
dc.identifier.urihttps://hdl.handle.net/1721.1/144452
dc.description.abstract<jats:p>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.</jats:p>en_US
dc.language.isoen
dc.publisherPublic Library of Science (PLoS)en_US
dc.relation.isversionof10.1371/journal.pone.0271947en_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourcePLoSen_US
dc.titleMachine-learning media biasen_US
dc.typeArticleen_US
dc.identifier.citationD’Alonzo, Samantha and Tegmark, Max. 2022. "Machine-learning media bias." PLOS ONE, 17 (8).
dc.contributor.departmentMassachusetts Institute of Technology. Department of Physics
dc.relation.journalPLOS ONEen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2022-08-26T12:43:16Z
dspace.orderedauthorsD’Alonzo, S; Tegmark, Men_US
dspace.date.submission2022-08-26T12:43:19Z
mit.journal.volume17en_US
mit.journal.issue8en_US
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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