Middle-Aged Video Consumers' Beliefs About Algorithmic Recommendations on YouTube

TL;DR

This study explores middle-aged YouTube users' beliefs about how their video recommendations are influenced, revealing limited understanding and identifying four belief groups and a new actor framework affecting recommendations.

Contribution

It introduces a novel framework distinguishing four actors influencing recommendations and highlights the impact of corporate decisions on user perceptions and interactions.

Findings

Users are aware of the recommendation system but have limited understanding.

Four belief groups identified: Previous Actions, Social Media, Recommender System, Company Policy.

A new actor framework includes user, others, algorithm, and organization.

Abstract

User beliefs about algorithmic systems are constantly co-produced through user interaction and the complex socio-technical systems that generate recommendations. Identifying these beliefs is crucial because they influence how users interact with recommendation algorithms. With no prior work on user beliefs of algorithmic video recommendations, practitioners lack relevant knowledge to improve the user experience of such systems. To address this problem, we conducted semi-structured interviews with middle-aged YouTube video consumers to analyze their user beliefs about the video recommendation system. Our analysis revealed different factors that users believe influence their recommendations. Based on these factors, we identified four groups of user beliefs: Previous Actions, Social Media, Recommender System, and Company Policy. Additionally, we propose a framework to distinguish the four main actors that users believe influence their video recommendations: the current user, other users, the algorithm, and the organization. This framework provides a new lens to explore design suggestions based on the agency of these four actors. It also exposes a novel aspect previously unexplored: the effect of corporate decisions on the interaction with algorithmic recommendations. While we found that users are aware of the existence of the recommendation system on YouTube, we show that their understanding of this system is limited.