City Research Online

Abstract

Prompted by the ongoing development of content personalization by social networks and mainstream news brands, and recent debates about balancing algorithmic and editorial selection, this study explores what audiences think about news selection mechanisms and why. Analysing data from a 26-country survey (N=53,314), we report the extent to which audiences believe story selection by editors and story selection by algorithms are good ways to get news online and, using multi-level models, explore the relationships that exist between individuals’ characteristics and those beliefs. The results show that, collectively, audiences believe algorithmic selection guided by a user’s past consumption behaviour is a better way to get news than editorial curation. There are, however, significant variations in these beliefs at the individual level. Age, trust in news, concerns about privacy, mobile news access, paying for news, and six other variables had effects. Our results are partly in line with current general theory on algorithmic appreciation, but diverge in our findings on the relative appreciation of algorithms and experts, and in how the appreciation of algorithms can differ according to the data that drive them. We believe this divergence is partly due to our study’s focus on news, showing algorithmic appreciation has context-specific characteristics.

Publication Type:	Article
Additional Information:	https://doi.org/10.1080/21670811.2018.1493936 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution- NonCommercial-No Derivatives License (http://creativecommons.org/licenses/by/4.0/), which permits non commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
Publisher Keywords:	algorithms; collaborative filtering; gatekeeping; journalistic curation; news selection; personalization; recommender systems; user tracking
Subjects:	H Social Sciences > HM Sociology P Language and Literature > PN Literature (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	School of Communication & Creativity > Journalism
SWORD Depositor:	Symplectic Administrator

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