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

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.