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Automating dynamic consent decisions for the processing of social media data in health research
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dc.contributor.author | Norval, Chris | |
dc.contributor.author | Henderson, Tristan | |
dc.date.accessioned | 2019-10-14T15:30:03Z | |
dc.date.available | 2019-10-14T15:30:03Z | |
dc.date.issued | 2020-07 | |
dc.identifier | 261204281 | |
dc.identifier | 71409855-ce01-460a-a0c4-804b411dd103 | |
dc.identifier | 85075129506 | |
dc.identifier | 000496047000001 | |
dc.identifier.citation | Norval , 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/10.1177/1556264619883715 | en |
dc.identifier.issn | 1556-2646 | |
dc.identifier.other | Bibtex: urn:65d34b7ada060f05b08c26f2a7cbf698 | |
dc.identifier.uri | https://hdl.handle.net/10023/18663 | |
dc.description | Funding: This work was supported by the Wellcome Trust [UNS19427]. | en |
dc.description.abstract | Social 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.format.extent | 1141981 | |
dc.language.iso | eng | |
dc.relation.ispartof | Journal of Empirical Research on Human Research Ethics | en |
dc.subject | Social media | en |
dc.subject | Privacy | en |
dc.subject | Informed consent | en |
dc.subject | Health support networks | en |
dc.subject | Contextual integrity | en |
dc.subject | BJ Ethics | en |
dc.subject | QA75 Electronic computers. Computer science | en |
dc.subject | R Medicine | en |
dc.subject | DAS | en |
dc.subject.lcc | BJ | en |
dc.subject.lcc | QA75 | en |
dc.subject.lcc | R | en |
dc.title | Automating dynamic consent decisions for the processing of social media data in health research | en |
dc.type | Journal article | en |
dc.contributor.sponsor | The Wellcome Trust | en |
dc.contributor.institution | University of St Andrews. School of Computer Science | en |
dc.contributor.institution | University of St Andrews. Centre for Research into Equality, Diversity & Inclusion | en |
dc.identifier.doi | 10.1177/1556264619883715 | |
dc.description.status | Peer reviewed | en |
dc.identifier.url | https://github.com/cnorval/automating-dynamic-consent-dataset | en |
dc.identifier.url | https://arxiv.org/abs/1910.05265 | en |
dc.identifier.grantnumber | N/A | en |
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