Social Learning and Content Quality Under Polarization

Problem definition : This paper studies how polarization influences content consumption and production on digital platforms that monetize consumer engagement. Specifically, we consider a content that advocates a particular position on a divisive issue. Consumers with polarized preferences toward the content’s position are sequentially exposed to the content. Initially, consumers are uncertain about the content quality, but they have the opportunity to learn about it using aggregate consumption metrics and other informative signals provided by the platform. Methodology/results : Using a stylized model, we find that under polarization, social learning based on consumption metrics can mislead consumers to perceive low-quality content as higher quality, even in the long run. Consequently, content providers may decrease their effort to improve content quality. These effects are amplified for more polarizing issues, especially when the content’s position is “mainstream” (i.e., aligned with the majority of the population). Our results thus provide a potential explanation for the proliferation of low-quality, polarizing content on platforms. Managerial implications : We offer normative guidance for content platforms seeking to enhance content quality, including offering consumers information to aid the social learning mechanism and appropriately selecting audience to mitigate the echo-chamber effect. Additionally, we propose payment schemes based on content popularity as an effective means to encourage content providers to improve quality.