A Value and Diversity-Aware News Recommendation Systems: Can Algorithmic Gatekeeping Nudge Readers to View Diverse News?

Abstract

Personalized news recommendation systems (NRSs) have become essential tools for users to view the vast amount of online news. Current NRSs, however, face crucial challenges in ensuring users’ right to view diverse news and viewpoints. We propose a conceptual framework for personalized recommendation nudges that can promote diverse news consumption on online platforms. We test the effects of diversity nudges by examining how users make sense of algorithmic nudges and how nudges influence users’ views on personalization and attitudes toward news diversity and media pluralism. The findings show that algorithmic nudges play a crucial role in understanding normative values in NRSs, which then influence the user’s intention to consume diverse news. The results further imply the personalization paradox that personalized news recommendations can enhance and decrease user engagement with the systems. The results provide conceptual and operational bases for diversity-aware NRS design, improving the diversity and personalization of news recommendations. We offer a conceptual framework of algorithmic nudges and news diversity, and from there, we develop theoretically grounded paths for facilitating diversity and pluralism in NRSs.