Management Information Systems Quarterly


In recent years, there has been a heated discussion on opinion polarization on social media platforms. Extant research attributes the emergence of echo chambers to higher exposure to information from users’ existing social networks, which consists of like-minded others and argues that the provision of information from outside users’ networks could alleviate opinion polarization. In this paper, we formulate a hierarchical Bayesian learning model to investigate the impact of replies, one of the main channels for information outside of users’ networks, on opinion polarization. We leverage a unique natural experiment contained in the data from a leading microblogging website in China in which the reply function was shut down for three days. This setting allows us to identify the impact of replies from that of peer microblogs. We found that shutting down reply function reduced sentiment polarization on the microblogging site. In addition, this effect was more significant for individuals with higher social media participation. The results of this study shed light on marketing campaign strategies as well as the ways in which platform design can reduce polarization.