Unveiling User Engagement Patterns on Stack Exchange Through Network Analysis

TL;DR

This study analyzes user engagement on Stack Exchange using network metrics, revealing community-specific dynamics and offering insights for improving user participation across different platforms.

Contribution

It introduces a network-based framework to analyze engagement patterns and compares dynamics across multiple Stack Exchange communities, highlighting differences between small and large platforms.

Findings

Smaller communities have more concentrated user influence.

Larger platforms exhibit more distributed engagement.

Insights into user roles and influence patterns.

Abstract

Stack Exchange, a question-and-answer(Q&A) platform, has exhibited signs of a declining user engagement. This paper investigates user engagement dynamics across various Stack Exchange communities including Data science, AI, software engineering, project management, and GenAI. We propose a network graph representing users as nodes and their interactions as edges. We explore engagement patterns through key network metrics including Degree Centerality, Betweenness Centrality, and PageRank. The study findings reveal distinct community dynamics across these platforms, with smaller communities demonstrating more concentrated user influence, while larger platforms showcase more distributed engagement. Besides, the results showed insights into user roles, influence, and potential strategies for enhancing engagement. This research contributes to understanding of online community behavior and provides a framework for future studies to improve the Stack Exchange user experience.