Journal of Social Computing 2020, 1(1): 40-52 https://doi.org/10.23919/JSC.2020.0005

Open Access | Issue | Published: 28 October 2020

Predicting Tie Strength of Chinese Guanxi by Using Big Data of Social Networks

Show Author's Information Hide Author's Information Xin Gao, Jar-Der Luo(

), Kunhao Yang, Xiaoming Fu, Loring Liu, Weiwei Gu

Department of Sociology, Tsinghua University, Beijing 100084, China.

Graduate School of Arts and Sciences, University of Tokyo, Tokyo 153-8902, Japan.

Institute of Computer Science, University of Göttingen, Göttingen 37077, Germany.

Tencent Computer System Co. Ltd, Shenzhen 518000, China.

Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.

Keywords:

social network, tie strength, Dunbar circle theory, Chinese Guanxi theory, supervised classification model

Cite this article:

Gao X, Luo J-D, Yang K, et al. Predicting Tie Strength of Chinese Guanxi by Using Big Data of Social Networks. Journal of Social Computing, 2020, 1(1): 40-52. https://doi.org/10.23919/JSC.2020.0005

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Abstract

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.

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Predicting Tie Strength of Chinese Guanxi by Using Big Data of Social Networks

Show Author's information Hide Author's Information Xin Gao, Jar-Der Luo(

), Kunhao Yang, Xiaoming Fu, Loring Liu, Weiwei Gu

Department of Sociology, Tsinghua University, Beijing 100084, China.

Graduate School of Arts and Sciences, University of Tokyo, Tokyo 153-8902, Japan.

Institute of Computer Science, University of Göttingen, Göttingen 37077, Germany.

Tencent Computer System Co. Ltd, Shenzhen 518000, China.

Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.

Abstract

Keywords: social network, tie strength, Dunbar circle theory, Chinese Guanxi theory, supervised classification model

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Acknowledgements

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Publication history

Received: 31 May 2020

Accepted: 05 July 2020

Published: 28 October 2020

Issue date: September 2020

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Acknowledgements

We are grateful for the financial support of Tencent Research Institute Project "Research on identification of opinion leaders based on QQ big data", project number: 20182001706, as well as the support of Tsinghua-Gottingen Student Exchange Project IDS-SSP-2017001.

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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).