Automating the News: How Personalized News Recommender System Design Choices Impact News Reception. (December 2014)

• Record Type: Journal Article
• Authors: Beam, Michael A.
• Is Part Of: Communication research. Volume 41:Number 8(2014:Dec.)
• Page Start: 1019
• Page End: 1041
• Publication Date: 2014-12
• Subjects: Internet news -- personalization -- selective exposure -- news information processing -- news knowledge; Communication -- Research -- Periodicals; 302.2
• DOI: 10.1177/0093650213497979
• Languages: English
• ISSNs: 0093-6502

Abstract

This study investigates the impact of personalized news recommender system design on selective exposure, elaboration, and knowledge. Scholars have worried that proliferation of personalization technologies will degrade public opinion by isolating people from challenging perspectives. Informed by selective exposure research, this study examines personalized news recommender system designs using a communication mediation model. Recommender system design choices examined include computer-generated personalized recommendations, user-customized recommendations, and full or limited news information environments based on recommendations. Results from an online mock election experiment with Ohio adult Internet users indicate increased selective exposure when using personalized news systems. However, portals recommending news based on explicit user customization result in significantly higher counterattitudinal news exposure. Expected positive effects on elaboration and indirect effects on knowledge through elaboration are found only in personalized news recommender systems that display only recommended headlines. Lastly, personalized news recommender system use has a negative direct effect on knowledge.