Uncovering the Dynamics of Crowdlearning and the Value of Knowledge
This work addresses the challenge of understanding and optimizing knowledge dynamics in online communities, which is incremental as it builds on existing models of learning and expertise.
The paper tackles the problem of modeling how users learn from and contribute to crowdlearning sites like Stack Overflow, developing a probabilistic framework that captures expertise evolution, forgetting, and both on-site and off-site learning, and finds that high-value answers are rare and that mid-range users acquire the most knowledge.
Learning from the crowd has become increasingly popular in the Web and social media. There is a wide variety of crowdlearning sites in which, on the one hand, users learn from the knowledge that other users contribute to the site, and, on the other hand, knowledge is reviewed and curated by the same users using assessment measures such as upvotes or likes. In this paper, we present a probabilistic modeling framework of crowdlearning, which uncovers the evolution of a user's expertise over time by leveraging other users' assessments of her contributions. The model allows for both off-site and on-site learning and captures forgetting of knowledge. We then develop a scalable estimation method to fit the model parameters from millions of recorded learning and contributing events. We show the effectiveness of our model by tracing activity of ~25 thousand users in Stack Overflow over a 4.5 year period. We find that answers with high knowledge value are rare. Newbies and experts tend to acquire less knowledge than users in the middle range. Prolific learners tend to be also proficient contributors that post answers with high knowledge value.