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dc.contributor.authorNorval, Chris
dc.contributor.authorHenderson, Tristan
dc.date.accessioned2019-10-14T15:30:03Z
dc.date.available2019-10-14T15:30:03Z
dc.date.issued2020-07
dc.identifier.citationNorval , C & Henderson , T 2020 , ' Automating dynamic consent decisions for the processing of social media data in health research ' , Journal of Empirical Research on Human Research Ethics , vol. 15 , no. 3 , pp. 187-201 . https://doi.org/10.1177/1556264619883715en
dc.identifier.issn1556-2646
dc.identifier.otherPURE: 261204281
dc.identifier.otherPURE UUID: 71409855-ce01-460a-a0c4-804b411dd103
dc.identifier.otherBibtex: urn:65d34b7ada060f05b08c26f2a7cbf698
dc.identifier.otherScopus: 85075129506
dc.identifier.otherWOS: 000496047000001
dc.identifier.urihttp://hdl.handle.net/10023/18663
dc.descriptionFunding: This work was supported by the Wellcome Trust [UNS19427].en
dc.description.abstractSocial media have become a rich source of data, particularly in health research. Yet, the use of such data raises significant ethical questions about the need for the informed consent of those being studied. Consent mechanisms, if even obtained, are typically broad and inflexible, or place a significant burden on the participant. Machine learning algorithms show much promise for facilitating a ‘middle ground approach: using trained models to predict and automate granular consent decisions. Such techniques, however, raise a myriad of follow-on ethical and technical considerations. In this paper, we present an exploratory user study (n= 67) in which we find that we can predict the appropriate flow of health-related social media data with reasonable accuracy, while minimising undesired data leaks. We then attempt to deconstruct the findings of this study, identifying and discussing a number of real-world implications if such a technique were put into practice
dc.language.isoeng
dc.relation.ispartofJournal of Empirical Research on Human Research Ethicsen
dc.rightsCopyright © 2019 the Author(s). This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1177/1556264619883715en
dc.subjectSocial mediaen
dc.subjectPrivacyen
dc.subjectInformed consenten
dc.subjectHealth support networksen
dc.subjectContextual integrityen
dc.subjectBJ Ethicsen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectR Medicineen
dc.subjectDASen
dc.subject.lccBJen
dc.subject.lccQA75en
dc.subject.lccRen
dc.titleAutomating dynamic consent decisions for the processing of social media data in health researchen
dc.typeJournal articleen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.contributor.institutionUniversity of St Andrews.Centre for Research into Equality, Diversity & Inclusionen
dc.identifier.doihttps://doi.org/10.1177/1556264619883715
dc.description.statusPeer revieweden
dc.identifier.urlhttps://github.com/cnorval/automating-dynamic-consent-dataseten
dc.identifier.urlhttps://arxiv.org/abs/1910.05265en


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