Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2018

Abstract

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).

Keywords

GitHub; Social collaborative platforms, Prediction, Stack overflow

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

WebSci' 18: Proceedings of the 10th ACM Conference on Web Science, Amsterdam, Netherlands, May 27-30

First Page

77

Last Page

86

ISBN

9781450355636

Identifier

10.1145/3201064.3201067

Publisher

ACM

City or Country

New York

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1145/3201064.3201067

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