With the growth of online commerce, companies have created virtual communities (VCs) where users can create posts and reply to posts about the company’s products. VCs can be represented as networks, with users as nodes and relationships between users as edges. Information propagates through edges. In VC studies, it is important to know how the number of topics concerning the product grows over time and what network features make a user more influential than others in the information-spreading process. The existing literature has not provided a quantitative method with which to determine key points during the topic emergence process. Also, few researchers have considered the link between multilayer physical features and the nodes’ spreading influence. In this paper, we present two new ideas to enrich network theory as applied to VCs: a novel application of an adjusted coefficient of determination to topic growth and an adjustment to the Jaccard coefficient to measure the connection between two users. A two-layer network model was first used to study the spread of topics through a VC. A random forest method was then applied to rank various factors that might determine an individual user’s importance in topic spreading through a VC. Our research provides insightful ways for enterprises to mine information from VCs.

中文翻译：

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信息在虚拟社区中的传播

随着在线商务的发展，公司创建了虚拟社区（VC），用户可以在其中创建帖子并回复有关公司产品的帖子。VC可以表示为网络，用户为节点，用户之间的关系为边缘。信息通过边缘传播。在VC研究中，重要的是要了解与产品有关的主题数量如何随时间增长，以及哪些网络功能使用户在信息传播过程中的影响力比其他人更大。现有文献尚未提供定量方法来确定主题出现过程中的关键点。而且，很少有研究人员考虑过多层物理特征与节点扩散影响之间的联系。在本文中，我们提出了两种新的思想来丰富应用于VC的网络理论：调整后的确定系数对主题增长的新应用以及对Jaccard系数的调整以测量两个用户之间的联系。首先使用两层网络模型来研究通过VC传播主题。然后应用随机森林方法对可能决定单个用户在通过VC传播主题的重要性的各种因素进行排名。我们的研究为企业从风险投资中挖掘信息提供了有见地的方法。然后应用随机森林方法对可能决定单个用户在通过VC传播主题的重要性的各种因素进行排名。我们的研究为企业从风险投资中挖掘信息提供了有见地的方法。然后应用随机森林方法对可能决定单个用户在通过VC传播主题的重要性的各种因素进行排名。我们的研究为企业从风险投资中挖掘信息提供了有见地的方法。