'Datafied' Reading

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Haapoja, Jesse
dc.contributor.author Lampinen, Airi
dc.date.accessioned 2018-12-10T10:23:54Z
dc.date.available 2018-12-10T10:23:54Z
dc.date.issued 2018-09-29
dc.identifier.citation Haapoja , J & Lampinen , A 2018 , 'Datafied' Reading : Framing behavioral data and algorithmic news recommendations . in NordiCHI 2018 : Revisiting the Life Cycle - Proceedings of the 10th Nordic Conference on Human-Computer Interaction . ASSOCIATION FOR COMPUTING MACHINERY , pp. 125-136 , Nordic Conference on Human-Computer Interaction , Oslo , Norway , 29/09/2018 . DOI: 10.1145/3240167.3240194 en
dc.identifier.isbn 9781450364379
dc.identifier.other PURE UUID: 9290a0f0-e5e9-4c67-8a73-6cac57f371de
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/datafied-reading(9290a0f0-e5e9-4c67-8a73-6cac57f371de).html
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85056574879&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/30173576/SCI_Haapoja_Datafield_Reading_NordiCHI2018Scoopinion_cameraready.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/35158
dc.description.abstract There are increasing concerns about how people discover news online and how algorithmic systems affect those discoveries. We investigate how individuals made sense of behavioral data and algorithmic recommendations in the context of a system that transformed their online reading activities into a new data source. We apply Goffman's frame analysis to a qualitative study of Scoopinion, a collaborative news recommender system that used tracked reading time to recommend articles from whitelisted websites. Based upon ten user interviews and one designer interview, we describe 1) the process through which reading was framed as a 'datafied' activity and 2) how behavioral data was interpreted as socially meaningful and communicative, even in the absence of overtly social system features, producing what we term 'implicit sociality'. We conclude with a discussion of how our findings about Scoopinion and its users speak to similar issues with more popular and more complex algorithmic systems. en
dc.format.extent 12
dc.format.extent 125-136
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartof Nordic Conference on Human-Computer Interaction en
dc.relation.ispartofseries NordiCHI 2018 en
dc.rights openAccess en
dc.subject.other Human-Computer Interaction en
dc.subject.other Computer Networks and Communications en
dc.subject.other Computer Vision and Pattern Recognition en
dc.subject.other Software en
dc.subject.other 113 Computer and information sciences en
dc.title 'Datafied' Reading en
dc.type A4 Artikkeli konferenssijulkaisussa fi
dc.description.version Peer reviewed en
dc.contributor.department Professorship Turpeinen M.
dc.contributor.department Stockholm University
dc.contributor.department Department of Computer Science en
dc.subject.keyword Algorithmic System
dc.subject.keyword Behavioral Data
dc.subject.keyword Datafication
dc.subject.keyword Frame analysis
dc.subject.keyword Online Journalism
dc.subject.keyword Recommender systems
dc.subject.keyword Human-Computer Interaction
dc.subject.keyword Computer Networks and Communications
dc.subject.keyword Computer Vision and Pattern Recognition
dc.subject.keyword Software
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201812106173
dc.identifier.doi 10.1145/3240167.3240194
dc.type.version acceptedVersion

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