Network metrics.

Virtual collaborative Q&A communities generate shared knowledge through the interaction of people and content. This knowledge is often fragmented, and its value as a collective, collaboratively formed product, is largely overlooked. Inspired by work on individual mental semantic networks, the current study explores the networks formed by user-added associative links as reflecting an aspect of self-organization within the communities’ collaborative knowledge sharing. Using eight Q&A topic-centered discussions from the Stack Exchange platform, it investigated how associative links form internal structures within the networks. Network analysis tools were used to derive topological indicator metrics of complex structures from associatively-linked networks. Similar metrics extracted from 1000 simulated randomly linked networks of comparable sizes and growth patterns were used to generate estimated sampling distributions through bootstrap resampling, and 99% confidence intervals were constructed for each metric. The discussion-network indicators were compared against these. Results showed that participant-added associative links largely led to networks that were more clustered, integrated, and included posts with more connections than those that would be expected in random networks of similar size and growth pattern. The differences were observed to increase over time. Also, the largest connected subgraphs within the discussion networks were found to be modular. Limited qualitative observations have also pointed to the impacts of external content-related events on the network structures. The findings strengthen the notion that the networks emerging from associative link sharing resemble other information networks that are characterized by internal structures suggesting self-organization, laying the ground for further exploration of collaborative linking as a form of collective knowledge organization. It underscores the importance of recognizing and leveraging this latent mechanism in both theory and practice.

虚拟协作问答社区通过用户与内容的交互生成共享知识。这类知识往往呈现碎片化特征，其作为集体协作生成产物的价值在很大程度上被忽视了。受个体心理语义网络相关研究的启发，本研究聚焦于用户添加的关联链接所构成的网络，以此映射社区协作知识共享过程中的自组织特性。本研究以Stack Exchange平台上8个以问答话题为核心的讨论社区为研究对象，探究关联链接如何在网络内部形成结构。研究借助网络分析工具，从关联链接构成的网络中提取复杂结构的拓扑指标度量值。研究人员从1000个规模、增长模式与目标网络相近的随机链接模拟网络中提取了相同的指标，通过bootstrap（自助法）重采样生成估计抽样分布，并为每个指标构建了99%置信区间。随后将讨论网络的指标与上述分布进行对比。结果显示，用户添加的关联链接所构建的网络，相较于规模与增长模式相近的随机网络，往往具有更高的聚类性、更强的整合性，且帖子的连接数更多。这类差异随时间推移愈发显著。此外，讨论网络中的最大连通子图呈现模块化特征。有限的质性观察结果也表明，外部内容相关事件会对网络结构产生影响。本研究结果进一步佐证了如下观点：由关联链接共享所形成的网络，与其他具备自组织内部结构的信息网络具有相似性，这为将协作式链接作为集体知识组织形式开展进一步研究奠定了基础。该研究同时强调了在理论与实践层面识别并利用这一潜在机制的重要性。