Unveiling User Engagement Patterns on Stack Exchange Through Network Analysis
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912027066236928 |
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| author | Saha, Agnik Kader, Mohammad Shahidul Masum, Mohammad |
| author_facet | Saha, Agnik Kader, Mohammad Shahidul Masum, Mohammad |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_08944 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unveiling User Engagement Patterns on Stack Exchange Through Network Analysis Saha, Agnik Kader, Mohammad Shahidul Masum, Mohammad Social and Information Networks 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. |
| title | Unveiling User Engagement Patterns on Stack Exchange Through Network Analysis |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2409.08944 |