The Spread of Information in Virtual Communities
| Autor: | Qingchun Meng, Zhen Zhang, Jin Du, Xiaoxia Rong, Xiaodan Fan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Multidisciplinary
Jaccard index General Computer Science Article Subject Computer science Process (engineering) 05 social sciences Rank (computer programming) 02 engineering and technology Network theory QA75.5-76.95 Data science Product (business) Electronic computers. Computer science 0502 economics and business 0202 electrical engineering electronic engineering information engineering Key (cryptography) 050211 marketing 020201 artificial intelligence & image processing Know-how Network model |
| Zdroj: | Complexity, Vol 2020 (2020) |
| ISSN: | 1099-0526 1076-2787 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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