Algorithmic News Content Personalization and Readers’ Attitudes
Yixue Wang, Aaron Shaw, Stephanie Edgerly, Darren Gergle, Nicholas A. Diakopoulos · Digital Journalism · 2026
Content personalization, providing personalized facts adapted to readers within the same articles, has become an emerging technology in online news. However, its theoretical examination remains largely under-explored. In this paper, we investigate readers’ perceptions of personalized articles compared to non-personalized ones. With a theoretical framework and valid responses from 249 participants recruited from Prolific, we analyze how content personalization affects audiences’ attitudes and engagement. We implemented a 3 (Personalization, Personalization with explanations of personalization, Control) * 2 (topics: mental health vs. food insecurity) factorial between-subject experiment design. The results reveal that readers’ attitudes and intent to engage were indirectly affected by content personalization through the perceived relevance of the article. While explanations of personalization (e.g., “The following sentence has been personalized based on your information.”) did not impact perceived article relevance or attitudes, they affect readers’ intentions to engage with the article. Participants’ qualitative feedback suggests that these explanations might create a sense of “creepiness,” which may negatively impact their attitudes toward the article. Our findings underscore the potential for news organizations to adopt the content personalization strategy we evaluated, and emphasize the pivotal role of amplifying readers’ perceived relevance in news delivery.