Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/29576
Full metadata record
DC FieldValueLanguage
dc.contributor.authorRowberry, Simonen_UK
dc.date.accessioned2019-05-25T00:03:16Z-
dc.date.available2019-05-25T00:03:16Z-
dc.date.issued2019-05en_UK
dc.identifier.urihttp://hdl.handle.net/1893/29576-
dc.description.abstractCompanies including Jellybooks and Amazon have introduced analytics to collect, analyze and monetize the user’s reading experience. Ebook apps and hardware collect implicit data about reading including progress and speed as well as encouraging readers to share more data through social networks. These practices generate large data sets with millions, if not billions of data points. For example, a copy of the King James Bible on the Kindle features over two million shared highlights. The allure of big data suggests that these metrics can be used at scale to gain a better understanding of how readers interact with books. While data collection practices continue to evolve, it is unclear how the metrics relate to the act of reading. For example, Kindle software tracks which words a reader looks up, but cannot distinguish between accidental look-ups, or otherwise link the act to the reader’s comprehension. In this article, I analyze patent filings and ebook software source code to assess the disconnect between data collection practices and the act of reading. The metrics capture data associated with software use rather than reading and therefore offer a poor approximation of the reading experience and must be corroborated by further data.en_UK
dc.language.isoenen_UK
dc.relationRowberry S (2019) The limits of Big Data for analyzing reading. Participations, 16 (1), pp. 237-257. http://www.participations.org/Volume%2016/Issue%201/12.pdfen_UK
dc.rightsPublisher allows this work to be made available in this repository. Published in Participations with the following policy: Copyright will always remain with authors, who are free to republish submissions, providing only that a proper acknowledgement of prior publication in Participations is included. We are happy for work to be placed in institutional repositories or individuals' websites on the same basis of acknowledgement. This article was published in Participations May 2019 (Volume 16, Issue 1, pp. 237-257 ): http://www.participations.org/Volume%2016/Issue%201/12.pdfen_UK
dc.subjectReader Analyticsen_UK
dc.subjectAmazonen_UK
dc.subjectKindleen_UK
dc.subjectEbooksen_UK
dc.subjectBig Dataen_UK
dc.subjectCritical Code Studiesen_UK
dc.subjectPatentsen_UK
dc.titleThe limits of Big Data for analyzing readingen_UK
dc.typeJournal Articleen_UK
dc.citation.jtitleParticipationsen_UK
dc.citation.issn1749-8716en_UK
dc.citation.volume16en_UK
dc.citation.issue1en_UK
dc.citation.spage237en_UK
dc.citation.epage257en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.identifier.urlhttp://www.participations.org/Volume%2016/Issue%201/12.pdfen_UK
dc.contributor.affiliationCommunications, Media and Cultureen_UK
dc.identifier.wtid1268847en_UK
dc.contributor.orcid0000-0002-4321-299Xen_UK
dc.date.accepted2019-03-07en_UK
dc.description.refREF Compliant by Deposit in Stirling's Repositoryen_UK
dc.date.filedepositdate2019-05-24en_UK
Appears in Collections:Communications, Media and Culture Journal Articles

Files in This Item:
File Description SizeFormat 
12.pdfFulltext - Published Version1.16 MBAdobe PDFView/Open


This item is protected by original copyright



Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

If you believe that any material held in STORRE infringes copyright, please contact library@stir.ac.uk providing details and we will remove the Work from public display in STORRE and investigate your claim.