The Spread of Information in Virtual Communities
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. I...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/6629318 |
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| _version_ | 1832550045148250112 |
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| author | Zhen Zhang Jin Du Qingchun Meng Xiaoxia Rong Xiaodan Fan |
| author_facet | Zhen Zhang Jin Du Qingchun Meng Xiaoxia Rong Xiaodan Fan |
| author_sort | Zhen Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-079e0291ef594b208ac2da5d3ef1d553 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-079e0291ef594b208ac2da5d3ef1d5532025-02-03T06:07:42ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/66293186629318The Spread of Information in Virtual CommunitiesZhen Zhang0Jin Du1Qingchun Meng2Xiaoxia Rong3Xiaodan Fan4Department of Statistics, The Chinese University of Hong Kong, Hong Kong SAR, ChinaDepartment of Statistics, The Chinese University of Hong Kong, Hong Kong SAR, ChinaSchool of Management, Shandong University, Jinan, ChinaSchool of Mathematics, Shandong University, Jinan, ChinaDepartment of Statistics, The Chinese University of Hong Kong, Hong Kong SAR, ChinaWith 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.http://dx.doi.org/10.1155/2020/6629318 |
| spellingShingle | Zhen Zhang Jin Du Qingchun Meng Xiaoxia Rong Xiaodan Fan The Spread of Information in Virtual Communities Complexity |
| title | The Spread of Information in Virtual Communities |
| title_full | The Spread of Information in Virtual Communities |
| title_fullStr | The Spread of Information in Virtual Communities |
| title_full_unstemmed | The Spread of Information in Virtual Communities |
| title_short | The Spread of Information in Virtual Communities |
| title_sort | spread of information in virtual communities |
| url | http://dx.doi.org/10.1155/2020/6629318 |
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