In social network analysis, a key issue is the detection of meaningful communities. This problem consists of finding groups of people who are both connected and semantically aligned. In the software development context, identifying communities considering both collaborations between developers and their skills can help to address critical elements or issues in a project. However, a large amount of data and the lack of data structure make it difficult to analyze these networks’ content. In this paper, we propose a framework for detecting overlapping semantic communities and their influential members. We also propose an ontology to extract topics of interest through tag enrichment in a Q&A forum. An evaluation was conducted in a large network of software developers built with Stack Overflow’s data, showing that the proposed framework and ontology can find real communities of developers. The evaluation indicates that their members are semantic aligned and still active in the detected topics of interest, and the quantitative analysis showed that the detected communities have high internal connectivity.

在社交网络分析中，一个关键问题是检测有意义的社区。这个问题包括寻找既有联系又有语义对齐的人群。在软件开发环境中，识别社区同时考虑开发人员之间的协作和他们的技能有助于解决项目中的关键要素或问题。然而，大量的数据和缺乏数据结构使得分析这些网络的内容变得困难。在本文中，我们提出了一个用于检测重叠语义社区及其有影响力的成员的框架。我们还提出了一个本体，通过问答论坛中的标签丰富来提取感兴趣的主题。在使用 Stack Overflow 数据构建的大型软件开发人员网络中进行了评估，表明所提出的框架和本体可以找到真正的开发者社区。评估表明，他们的成员在检测到的兴趣主题中是语义一致的并且仍然活跃，并且定量分析表明检测到的社区具有很高的内部连通性。