Wisdom in Sum of Parts: Multi-Platform Activity Prediction in Social Collaborative Sites
This work addresses activity prediction for users in software development communities, but it is incremental as it builds on existing interest-based methods by adding cross-platform integration.
The paper tackles the problem of predicting user activities on social collaborative platforms by proposing a framework that uses inferred user interests from activities within and across platforms, achieving best accuracies with AUC=0.75 for GitHub and AUC=0.89 for Stack Overflow.
In this paper, we proposed a novel framework which uses user interests inferred from activities (a.k.a., activity interests) in multiple social collaborative platforms to predict users' platform activities. Included in the framework are two prediction approaches: (i) direct platform activity prediction, which predicts a user's activities in a platform using his or her activity interests from the same platform (e.g., predict if a user answers a given Stack Overflow question using the user's interests inferred from his or her prior answer and favorite activities in Stack Overflow), and (ii) cross-platform activity prediction, which predicts a user's activities in a platform using his or her activity interests from another platform (e.g., predict if a user answers a given Stack Overflow question using the user's interests inferred from his or her fork and watch activities in GitHub). To evaluate our proposed method, we conduct prediction experiments on two widely used social collaborative platforms in the software development community: GitHub and Stack Overflow. Our experiments show that combining both direct and cross-platform activity prediction approaches yield the best accuracies for predicting user activities in GitHub (AUC=0.75) and Stack Overflow (AUC=0.89).