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The Atlas of Chinese World Wide Web Ecosystem Shaped by the Collective Attention Flows
The web can be regarded as an ecosystem of digital resources connected and shaped by collective successive behaviors of users. Knowing how people allocate limited attention on different resources is of great importance. To answer this, we embed the most popular Chinese web sites into a high dimensio...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5094834/ https://www.ncbi.nlm.nih.gov/pubmed/27812133 http://dx.doi.org/10.1371/journal.pone.0165240 |
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author | Lou, Xiaodan Li, Yong Gu, Weiwei Zhang, Jiang |
author_facet | Lou, Xiaodan Li, Yong Gu, Weiwei Zhang, Jiang |
author_sort | Lou, Xiaodan |
collection | PubMed |
description | The web can be regarded as an ecosystem of digital resources connected and shaped by collective successive behaviors of users. Knowing how people allocate limited attention on different resources is of great importance. To answer this, we embed the most popular Chinese web sites into a high dimensional Euclidean space based on the open flow network model of a large number of Chinese users’ collective attention flows, which both considers the connection topology of hyperlinks between the sites and the collective behaviors of the users. With these tools, we rank the web sites and compare their centralities based on flow distances with other metrics. We also study the patterns of attention flow allocation, and find that a large number of web sites concentrate on the central area of the embedding space, and only a small fraction of web sites disperse in the periphery. The entire embedding space can be separated into 3 regions(core, interim, and periphery). The sites in the core (1%) occupy a majority of the attention flows (40%), and the sites (34%) in the interim attract 40%, whereas other sites (65%) only take 20% flows. What’s more, we clustered the web sites into 4 groups according to their positions in the space, and found that similar web sites in contents and topics are grouped together. In short, by incorporating the open flow network model, we can clearly see how collective attention allocates and flows on different web sites, and how web sites connected each other. |
format | Online Article Text |
id | pubmed-5094834 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-50948342016-11-18 The Atlas of Chinese World Wide Web Ecosystem Shaped by the Collective Attention Flows Lou, Xiaodan Li, Yong Gu, Weiwei Zhang, Jiang PLoS One Research Article The web can be regarded as an ecosystem of digital resources connected and shaped by collective successive behaviors of users. Knowing how people allocate limited attention on different resources is of great importance. To answer this, we embed the most popular Chinese web sites into a high dimensional Euclidean space based on the open flow network model of a large number of Chinese users’ collective attention flows, which both considers the connection topology of hyperlinks between the sites and the collective behaviors of the users. With these tools, we rank the web sites and compare their centralities based on flow distances with other metrics. We also study the patterns of attention flow allocation, and find that a large number of web sites concentrate on the central area of the embedding space, and only a small fraction of web sites disperse in the periphery. The entire embedding space can be separated into 3 regions(core, interim, and periphery). The sites in the core (1%) occupy a majority of the attention flows (40%), and the sites (34%) in the interim attract 40%, whereas other sites (65%) only take 20% flows. What’s more, we clustered the web sites into 4 groups according to their positions in the space, and found that similar web sites in contents and topics are grouped together. In short, by incorporating the open flow network model, we can clearly see how collective attention allocates and flows on different web sites, and how web sites connected each other. Public Library of Science 2016-11-03 /pmc/articles/PMC5094834/ /pubmed/27812133 http://dx.doi.org/10.1371/journal.pone.0165240 Text en © 2016 Lou et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Lou, Xiaodan Li, Yong Gu, Weiwei Zhang, Jiang The Atlas of Chinese World Wide Web Ecosystem Shaped by the Collective Attention Flows |
title | The Atlas of Chinese World Wide Web Ecosystem Shaped by the Collective Attention Flows |
title_full | The Atlas of Chinese World Wide Web Ecosystem Shaped by the Collective Attention Flows |
title_fullStr | The Atlas of Chinese World Wide Web Ecosystem Shaped by the Collective Attention Flows |
title_full_unstemmed | The Atlas of Chinese World Wide Web Ecosystem Shaped by the Collective Attention Flows |
title_short | The Atlas of Chinese World Wide Web Ecosystem Shaped by the Collective Attention Flows |
title_sort | atlas of chinese world wide web ecosystem shaped by the collective attention flows |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5094834/ https://www.ncbi.nlm.nih.gov/pubmed/27812133 http://dx.doi.org/10.1371/journal.pone.0165240 |
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