'Datafied' Reading: Framing behavioral data and algorithmic news recommendations

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Researchers

Research units

  • Stockholm University

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.

Details

Original languageEnglish
Title of host publicationNordiCHI 2018
Subtitle of host publicationRevisiting the Life Cycle - Proceedings of the 10th Nordic Conference on Human-Computer Interaction
Publication statusPublished - 29 Sep 2018
MoE publication typeA4 Article in a conference publication
EventNordic Conference on Human-Computer Interaction - Oslo, Norway
Duration: 29 Sep 20183 Oct 2018
Conference number: 10

Conference

ConferenceNordic Conference on Human-Computer Interaction
Abbreviated titleNordiCHI
CountryNorway
CityOslo
Period29/09/201803/10/2018

    Research areas

  • Algorithmic System, Behavioral Data, Datafication, Frame analysis, Online Journalism, Recommender systems

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