Predicting Tie Strength of Chinese Guanxi by Using Big Data of Social Networks
This paper poses a question: How many types of social relations can be categorized in the Chinese context? In social networks, the calculation of tie strength can better represent the degree of intimacy of the relationship between nodes, rather than just indicating whether the link exists or not. Pr...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2020-09-01
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| Series: | Journal of Social Computing |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.23919/JSC.2020.0005 |
| _version_ | 1811344611343335424 |
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| author | Xin Gao Jar-Der Luo Kunhao Yang Xiaoming Fu Loring Liu Weiwei Gu |
| author_facet | Xin Gao Jar-Der Luo Kunhao Yang Xiaoming Fu Loring Liu Weiwei Gu |
| author_sort | Xin Gao |
| collection | DOAJ |
| description | This paper poses a question: How many types of social relations can be categorized in the Chinese context? In social networks, the calculation of tie strength can better represent the degree of intimacy of the relationship between nodes, rather than just indicating whether the link exists or not. Previou research suggests that Granovetter measures tie strength so as to distinguish strong ties from weak ties, and the Dunbar circle theory may offer a plausible approach to calculating 5 types of relations according to interaction frequency via unsupervised learning (e.g., clustering interactive data between users in Facebook and Twitter). In this paper, we differentiate the layers of an ego-centered network by measuring the different dimensions of user's online interaction data based on the Dunbar circle theory. To label the types of Chinese guanxi, we conduct a survey to collect the ground truth from the real world and link this survey data to big data collected from a widely used social network platform in China. After repeating the Dunbar experiments, we modify our computing methods and indicators computed from big data in order to have a model best fit for the ground truth. At the same time, a comprehensive set of effective predictors are selected to have a dialogue with existing theories of tie strength. Eventually, by combining Guanxi theory with Dunbar circle studies, four types of guanxi are found to represent a four-layer model of a Chinese ego-centered network. |
| first_indexed | 2024-04-13T19:50:43Z |
| format | Article |
| id | doaj.art-1b5b2679846f4f18b90c98dd05e81583 |
| institution | Directory Open Access Journal |
| issn | 2688-5255 |
| language | English |
| last_indexed | 2024-04-13T19:50:43Z |
| publishDate | 2020-09-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Journal of Social Computing |
| spelling | doaj.art-1b5b2679846f4f18b90c98dd05e815832022-12-22T02:32:33ZengTsinghua University PressJournal of Social Computing2688-52552020-09-0111405210.23919/JSC.2020.0005Predicting Tie Strength of Chinese Guanxi by Using Big Data of Social NetworksXin Gao0Jar-Der Luo1Kunhao Yang2Xiaoming Fu3Loring Liu4Weiwei Gu5<institution content-type="dept">Department of Sociology</institution>, <institution>Tsinghua University</institution>, <city>Beijing</city> <postal-code>100084</postal-code>, <country>China</country>.<institution content-type="dept">Department of Sociology</institution>, <institution>Tsinghua University</institution>, <city>Beijing</city> <postal-code>100084</postal-code>, <country>China</country>.<institution content-type="dept">Graduate School of Arts and Sciences</institution>, <institution>University of Tokyo</institution>, <city>Tokyo</city> <postal-code>153-8902</postal-code>, <country>Japan</country>.<institution content-type="dept">Institute of Computer Science</institution>, <institution>University of Göttingen</institution>, <city>Göttingen</city> <postal-code>37077</postal-code>, <country>Germany</country>.<institution>Tencent Computer System Co. Ltd</institution>, <city>Shenzhen</city> <postal-code>518000</postal-code>, <country>China</country>.<institution content-type="dept">Information Science and Technology</institution>, <institution>Beijing University of Chemical Technology</institution>, <city>Beijing</city> <postal-code>100029</postal-code>, <country>China</country>.This paper poses a question: How many types of social relations can be categorized in the Chinese context? In social networks, the calculation of tie strength can better represent the degree of intimacy of the relationship between nodes, rather than just indicating whether the link exists or not. Previou research suggests that Granovetter measures tie strength so as to distinguish strong ties from weak ties, and the Dunbar circle theory may offer a plausible approach to calculating 5 types of relations according to interaction frequency via unsupervised learning (e.g., clustering interactive data between users in Facebook and Twitter). In this paper, we differentiate the layers of an ego-centered network by measuring the different dimensions of user's online interaction data based on the Dunbar circle theory. To label the types of Chinese guanxi, we conduct a survey to collect the ground truth from the real world and link this survey data to big data collected from a widely used social network platform in China. After repeating the Dunbar experiments, we modify our computing methods and indicators computed from big data in order to have a model best fit for the ground truth. At the same time, a comprehensive set of effective predictors are selected to have a dialogue with existing theories of tie strength. Eventually, by combining Guanxi theory with Dunbar circle studies, four types of guanxi are found to represent a four-layer model of a Chinese ego-centered network.https://www.sciopen.com/article/10.23919/JSC.2020.0005tie strengthdunbar circle theorychinese guanxi theorysupervised classification modelsocial network |
| spellingShingle | Xin Gao Jar-Der Luo Kunhao Yang Xiaoming Fu Loring Liu Weiwei Gu Predicting Tie Strength of Chinese Guanxi by Using Big Data of Social Networks Journal of Social Computing tie strength dunbar circle theory chinese guanxi theory supervised classification model social network |
| title | Predicting Tie Strength of Chinese Guanxi by Using Big Data of Social Networks |
| title_full | Predicting Tie Strength of Chinese Guanxi by Using Big Data of Social Networks |
| title_fullStr | Predicting Tie Strength of Chinese Guanxi by Using Big Data of Social Networks |
| title_full_unstemmed | Predicting Tie Strength of Chinese Guanxi by Using Big Data of Social Networks |
| title_short | Predicting Tie Strength of Chinese Guanxi by Using Big Data of Social Networks |
| title_sort | predicting tie strength of chinese guanxi by using big data of social networks |
| topic | tie strength dunbar circle theory chinese guanxi theory supervised classification model social network |
| url | https://www.sciopen.com/article/10.23919/JSC.2020.0005 |
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