My Friends, Editors, Algorithms, and I

TLDR

Content personalization by social networks and mainstream news brands has spurred debates on balancing algorithmic and editorial news selection. The study investigates audience perceptions of algorithmic versus editorial news selection and the reasons behind them. Using a 26‑country survey of 53,314 respondents and multi‑level models, the authors examined how individual characteristics relate to beliefs about editorial and algorithmic news selection. Overall, respondents preferred algorithmic selection based on past consumption over editorial curation, though beliefs varied by age, trust, privacy concerns, mobile access, payment status, and other factors, revealing context‑specific differences from general theory.

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.