Wisdom in sum of parts: Multi-platform activity prediction in social collaborative sites

• Main Authors: LEE, Ka Wei, Roy, LO, David
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: Social Collaborative Platforms; Prediction; Stack Overflow; GitHub; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/4288; https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=5291&context=sis_research
• Institution: Singapore Management University

In this paper, we proposed a novel framework which uses user interests inferred from activities (a.k.a., activity interests) in multiplesocial collaborative platforms to predict users’ platform activities.Included in the framework are two prediction approaches: (i) directplatform activity prediction, which predicts a user’s activities in aplatform using his or her activity interests from the same platform(e.g., predict if a user answers a given Stack Overflow questionusing the user’s interests inferred from his or her prior answer andfavorite activities in Stack Overflow), and (ii) cross-platform activityprediction, which predicts a user’s activities in a platform using hisor her activity interests from another platform (e.g., predict if a useranswers a given Stack Overflow question using the user’s interestsinferred from his or her fork and watch activities in GitHub). Toevaluate our proposed method, we conduct prediction experimentson two widely used social collaborative platforms in the softwaredevelopment community: GitHub and Stack Overflow. Our experiments show that combining both direct and cross platform activityprediction approaches yield the best accuracies for predicting useractivities in GitHub (AUC=0.75) and Stack Overflow (AUC=0.89).