Learning When Reading: Evidence from an Online Mobile Reading Platform
利用中国在线阅读平台数据，构建贝叶斯学习模型，研究消费过程中的实时评论如何帮助用户评估书籍质量并做出逐章购买决策。
How do consumers listen to peers during consumption? On emerging digital platforms, real-time comments embedded within content—such as books or videos—allow users to observe others’ spontaneous reactions while engaging with the same material. Using data from a leading Chinese online reading platform, we develop a Bayesian learning model to quantify how these in-consumption comments help users learn book quality and make sequential chapter-by-chapter purchase decisions. Results show that not all comments are viewed equally: those reflecting plot-based insight or strong narrative engagement enhance perceived quality, whereas speculative or even purely cheerful comments may diminish it. Comment consistency—not just volume—plays a critical role in sustaining user engagement. For authors, stabilizing chapter quality and adopting informative chapter titles can encourage continuous reading. Platforms, in turn, can boost retention by encouraging consistent contributions of “favorable” in-consumption comments—those providing scene-based insights, character evaluations, or pleas for new chapters.