Quantity vs Diversity in Online Content Production: Evidence from a Knowledge Sharing Platform

Abstract

Online question-and-answer (Q&A) platforms, as an important type of user generated contents (UGC), allow users to learn and share different perspectives of information and knowledge. Such platforms' success critically depends on the quantity and diversity of the knowledge contents. This paper utilizes a novel dataset from one of the largest Q&A platforms and studies how the amount of information and the quality of the early-stage knowledge content influence the growth of future knowledge content. We measure and characterize knowledge content's growth in quantity and diversity using an unsupervised learning method (Doc2Vec), which allows us to account for similarity across documents based on their overall meaning. Our empirical results suggest the amount of information in the early knowledge content has a negative effect on the quantity of future knowledge content but a positive effect on diversity. We also find high-quality early knowledge content drives more future knowledge quantity but has no influence on diversity. Our analysis provides important managerial implications for platform strategies.

Keywords: question-and-answer platform, quantity-diversity tradeoff, unsupervised learning, Doc2Vec

JEL Classification: D26, L86, M31