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dc.contributor.authorMatz, SC
dc.contributor.authorKosinski, M
dc.contributor.authorNave, G
dc.contributor.authorStillwell, DJ
dc.date.accessioned2018-09-27T14:10:44Z
dc.date.available2018-09-27T14:10:44Z
dc.date.issued2018-06
dc.identifier.issn1091-6490
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/282812
dc.description.abstractWe thank Eckles et al. (1) for their thoughtful comments. The authors point out that the optimization algorithms of Facebook’s advertising platform constitute a potential confound of campaign outcomes. We agree, in general, that such algorithms could pose a threat to the validity of field studies since they introduce unintended variance across the target audiences. However, as we demonstrate below, it is unlikely that such confounds account for the pattern of results presented in our original research (2).
dc.languageeng
dc.publisherNational Academy of Sciences
dc.titleReply to Eckles et al.: Facebook's optimization algorithms are highly unlikely to explain the effects of psychological targeting
dc.typeArticle
prism.endingPageE5257
prism.issueIdentifier23
prism.publicationDate2018
prism.publicationNameProceedings of the National Academy of Sciences of the United States of America
prism.startingPageE5256
prism.volume115
dc.identifier.doi10.17863/CAM.30176
dcterms.dateAccepted2018-05-18
rioxxterms.versionofrecord10.1073/pnas.1806854115
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2018-06
dc.contributor.orcidStillwell, David [0000-0003-0174-3212]
dc.identifier.eissn1091-6490
rioxxterms.typeJournal Article/Review
cam.issuedOnline2018-05-18
rioxxterms.freetoread.startdate2019-05-18


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