Title: What Types of Crowds Generate More Valuable Content? Evidence from Cross-Platform Posting
Abstract: This study examines the value of content generated by various groups of users on a financial social media platform. The value of such content is measured by the incremental accuracy of predicting stock volatility based on the cues from those content. We argue that the characteristics of a crowd such as crowd size, crowd diversity, and crowd independence have significant impact on the predictive value of the crowd-generated content. Leveraging a natural experiment setup where the financial platform no longer receives cross-postings from another major social media platform, we show empirical evidence that the composition of crowds (i.e., characteristics of the users who make up crowds) does matter. Furthermore, the impacts of the characteristic features on the value of crowd-generated content are likely heterogeneous and non-monotone. Finally, we discuss the implications of this study about digital platform envelopment and competition.