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Unveiling User Engagement Patterns on Stack Exchange Through Network Analysis

Agnik Saha, Mohammad Shahidul Kader, Mohammad Masum

TL;DR

This paper investigates user engagement dynamics across various Stack Exchange communities including Data science, AI, software engineering, project management, project management, and GenAI and proposes a network graph representing users as nodes and their interactions as edges.

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.

Unveiling User Engagement Patterns on Stack Exchange Through Network Analysis

TL;DR

This paper investigates user engagement dynamics across various Stack Exchange communities including Data science, AI, software engineering, project management, project management, and GenAI and proposes a network graph representing users as nodes and their interactions as edges.

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.
Paper Structure (24 sections, 1 equation, 2 figures, 5 tables)

This paper contains 24 sections, 1 equation, 2 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Activity theory
  4. Research on Stack Exchange Platforms
  5. Research on User engagement
  6. Methodology
  7. Dataset Preparation and Analysis
  8. Questioner Responder (QR) Network
  9. Network Analysis
  10. Statistical Analysis of Centrality Measures Across Stack Exchange Platforms
  11. Description of Centrality Correlation Matrices
  12. Data Science
  13. Artificial Intelligence
  14. Project Management
  15. Generative AI
  16. ...and 9 more sections

Figures (2)

  • Figure 1: Conceptual Framework for User Engagement on Stack Exchange Platforms, integrating components of Activity Theory with Research Approach and Methodology. This framework illustrates the interactions between users (both questioners and responders), and the overall community structure, emphasizing how these elements influence user engagement and network dynamics.
  • Figure 2: Visualization of the Questioner-Responder Network from the GenAI StackExchange Platform. Nodes represent users, with directed edges indicating the flow of questions and responses. Source nodes (in blue) are users who asked questions, while target nodes (in coral) are users who provided responses. This graph illustrates the interaction dynamics within the community