Personalization, Engagement, and Content Quality on Social Media: An Evaluation of Reddit’s News Feed

Digital platforms increasingly curate their content through personalized algorithmic feeds. Platforms have an incentive to promote content that increases the predicted engagement of each user to lift advertising revenues. This paper studies how ranking content to maximize engagement affects the credibility of news content with which users engage. In addition, I evaluate how the ranking algorithm itself can be designed to promote engagement with high-credibility content. Using data from the Reddit politics community, I exploit a novel discontinuity in the ranking algorithm to identify the causal effect of a post’s rank on the number of comments it receives. I use this discontinuity to identify a model of user comment decisions and estimate the credibility of news content that users engage with under a personalized engagement-maximizing algorithm. The personalized engagement-maximizing algorithm exacerbates differences in the credibility of news content with which users engage. I then evaluate a credibility-aware algorithm that explicitly promotes credible news publishers and find the platform can substantially increase the share of engagement with high-credibility publishers for a small reduction in total engagement. These findings suggest algorithmic interventions can be a useful tool for managers to balance engagement quantity and content quality. This paper was accepted by Hemant Bhargava, information systems. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2024.06298 .