Wisdom in Sum of Parts: Multi-Platform Activity Prediction in Social Collaborative Sites

TL;DR

This paper introduces a framework for predicting user activities across social collaborative platforms by leveraging inferred user interests, demonstrating improved accuracy through combined direct and cross-platform prediction methods.

Contribution

It presents a novel multi-platform activity prediction framework that integrates direct and cross-platform interest-based predictions for social collaborative sites.

Findings

Combined prediction approaches improve accuracy (AUC=0.75 for GitHub, 0.89 for Stack Overflow).

Cross-platform interest inference enhances activity prediction.

The framework effectively leverages multi-platform user activity data.

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).