Citation: Beam, M. A. (2014). Automating the news: How personalized news recommender system design choices impact news reception. Communication Research.

DOI: 10.1177/0093650213497979

PDF: download [pre-press]

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 counter-attitudinal news exposure. Expected positive effects on elaboration and indirect effects on knowledge through elaboration are found only in personalized news recommender systems which display only recommended headlines. Lastly, personalized news recommender system use has a negative direct effect on knowledge.

Supplementary Materials:
Appendix 1: Personalized Algorithm Source Code
Appendix 2: Descriptive Statistics