Abstract
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.
Highlights
Virtual communities (VCs) provide an interactive experience that, if positive, may instil customer loyalty [1]
Complex network theory has been a major tool in the study of the physical structure and dynamic processes of social, biological, and technological networks [5]
Consumer VCs are different from traditional social networks in that they centre around products
Summary
Virtual communities (VCs) provide an interactive experience that, if positive, may instil customer loyalty [1]. Mining information provided by consumers in VCs enables companies to adjust the generation of their products to improve customer satisfaction [4]. Consumer VCs are different from traditional social networks in that they centre around products They provide a real-time look into customers’ experiences with a product from the date of release. A two-layered network model, representing two ways of spreading information, was introduced to study the spread of a topic through a VC and identify the most influential users. Simulations carried out to identify key users in the network are described, with the random forest method used to find features important to users’ information-spreading performance.
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