Modeling information sharing behavior on Q&A forums

Abstract

Q&A forums pool massive amounts of crowd expertise from a broad spectrum of geographical, cultural, and disciplinary knowledge toward specific, user-posed questions. Existing studies on these forums focus on how to route questions to the best answerers based on content or predict whether a question will be answered, but few of them investigated the inherent knowledge sharing relationship among users. We study knowledge sharing among users of StackOverflow, a popular Q&A forum, where the knowledge sharing process is related to the time elapsed since a question was posted, the reputation of the questioner, and the content of the posted text. Taking these factors into consideration, the paper proposes time-based information sharing model (TISM), where the likelihood a user will share or provide knowledge to another is modeled as a continuous function of time, reputation, and post length. With the resulting knowledge sharing network learned by TISM, we are able to predict for a given question the number of responses over time, who will answer the question and who will provide the accepted answer. Our experiments show that predictions using TISM outperform NetRate, query likelihood language, random forest, and linear regression models.