Social Learning with Polarized Preferences on Content Platforms

Motivated by a number of socioeconomic and political issues that attract competing (polarizing) beliefs in the society, this paper studies the impact of such beliefs on content consumption and production on a platform. Consumers are initially uninformed about the content quality but have the opportunity to learn about it using aggregate consumption metrics and other informative signals on the platform prior to consuming the content. Based on a stylized model, we find that a social learning (SL) mechanism based on aggregate consumption metrics can mislead consumers to incorrectly perceive low-quality content to be of higher quality. We find that the platform’s and the content provider’s preference for SL may not be perfectly aligned with those of consumers and we characterize parametric regimes where a conflict of interest arises. In particular, SL may lower the incentive for the content provider to improve the content quality, and by extension, the platform may prefer to facilitate SL by displaying consumption metrics to mask the underlying low quality of the content. These findings continue to hold when the platform selectively recommends content to consumers whose beliefs the content supports. A more accurate content recommendation policy improves the SL outcome, but it may also lower the incentive for the content provider to improve quality due to the formation of a so-called "echo-chamber."