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The structure and dynamics of knowledge network in domain-specific Q&A sites: a case study of stack overflow

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Abstract

Programming-specific Q&A sites (e.g., Stack Overflow) are being used extensively by software developers for knowledge sharing and acquisition. Due to the cross-reference of questions and answers (note that users also reference URLs external to the Q&A site. In this paper, URL sharing refers to internal URLs within the Q&A site, unless otherwise stated), knowledge is diffused in the Q&A site, forming a large knowledge network. In Stack Overflow, why do developers share URLs? How is the community feedback to the knowledge being shared? What are the unique topological and semantic properties of the resulting knowledge network in Stack Overflow? Has this knowledge network become stable? If so, how does it reach to stability? Answering these questions can help the software engineering community better understand the knowledge diffusion process in programming-specific Q&A sites like Stack Overflow, thereby enabling more effective knowledge sharing, knowledge use, and knowledge representation and search in the community. Previous work has focused on analyzing user activities in Q&A sites or mining the textual content of these sites. In this article, we present a methodology to analyze URL sharing activities in Stack Overflow. We use open coding method to analyze why users share URLs in Stack Overflow, and develop a set of quantitative analysis methods to study the structural and dynamic properties of the emergent knowledge network in Stack Overflow. We also identify system designs, community norms, and social behavior theories that help explain our empirical findings. Through this study, we obtain an in-depth understanding of the knowledge diffusion process in Stack Overflow and expose the implications of URL sharing behavior for Q&A site design, developers who use crowdsourced knowledge in Stack Overflow, and future research on knowledge representation and search.

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Notes

  1. In network science, assortative mixing is a bias in favor of connections between nodes with similar characteristics; disassortative mixing is a bias in favor of connections between dissimilarly characterized nodes. (Newman 2002)

  2. Modularity is one measure of network structure. A detected module is also called a community or a cluster. Networks with high modularity have dense connections between the nodes within modules but sparse connections between nodes in different modules.

  3. We do not list the detailed answer-to-question distribution, since the focus of this work is on those non-isolated knowledge units and their associations.

  4. How do I ask a good question? http://stackoverflow.com/help/how-to-ask

  5. How does Stack Overflow handle spam? http://meta.stackexchange.com/q/2765

  6. Visit the web service we built http://knowledge-so.appspot.com to see more examples.

  7. The results of other top N knowledge units are similar. Visit our web service at http://knowledge-so.appspot.com to see more results.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Deheng Ye, Zhenchang Xing & Nachiket Kapre

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  1. Deheng Ye
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  2. Zhenchang Xing
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Correspondence to Deheng Ye.

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Communicated by: Helen Sharp

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Ye, D., Xing, Z. & Kapre, N. The structure and dynamics of knowledge network in domain-specific Q&A sites: a case study of stack overflow. Empir Software Eng 22, 375–406 (2017). https://doi.org/10.1007/s10664-016-9430-z

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