Agency Aspirations: Understanding Users' Preferences And Perceptions Of Their Role In Personalised News Curation
Anna Marie Rezk, Auste Simkute, Ewa Luger, John Vines, Chris Elsden, Michael Evans, Rhianne Jones
Abstract
Recommender systems are increasingly employed by journalistic outlets to deliver personalised news, transforming news curation into a reciprocal yet insufficiently defined process influenced by editors, recommender systems, and individual user actions. To understand the tension in this dynamic and users’ preferences and perceptions of their role in personalised news curation, we conducted a study with UK participants aged 16-34. Building on a preliminary survey and interview study, which revealed a strong desire from participants for increased agency in personalisation, we designed an interactive news recommender provotype (provocative design artefact) which probed the role of agency in news curation with participants (n=16). Findings highlighted a behaviour-intention gap, indicating participants desire for agency yet reluctance to intervene actively in personalisation. Our research offers valuable insights into how users perceive their agency in personalised news curation, underscoring the importance for systems to be designed to support individuals becoming active agents in news personalisation.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext c2b87091-cad2-4d4f-9762-39ca54bc822aCited by top-tier papers2
- Exploring Collaboration Patterns and Strategies in Human-AI Co-creation through the Lens of Agency: A Scoping Review of the Top-tier HCI LiteratureShuning Zhang, Hui Wang, Xin YiCSCW 2025 · 31 citations
- Rethinking User Empowerment in AI Recommender System: Innovating Transparent and Controllable InterfacesMengke Wu, Weizi Liu, Yanyun Wang, Weiyu Ding et al.CHI 2026 · 3 citations
Builds on2
Related papers
- How Should AI Systems Talk to Users when Collecting their Personal Information? Effects of Role Framing and Self-Referencing on Human-AI InteractionMengqi Liao, S. Shyam SundarCHI 2021 · 33 citations
- Understanding the Gap Between Stated and Revealed Preferences in Social Media News FeedsDo Won Kim, Cody Buntain, Giovanni Luca CiampagliaCSCW 2026
- ONeRec: Towards Openness-Aware and Adaptive Proactive News RecommendationJie Li, Zhen Cui, Linmei HuWWW 2026
- Understanding User Perception of Automated News Generation SystemChanghoon Oh, Jinhan Choi, Sungwoo Lee, SoHyun Park et al.CHI 2020 · 32 citations
- 'Transparency is Meant for Control' and Vice Versa: Learning from Co-designing and Evaluating Algorithmic News RecommendersElias Storms, Oscar Alvarado, Luciana Monteiro KrebsCSCW 2022 · 29 citations
