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Big Data Mining and Analytics 2018, 1(4): 284-296 https://doi.org/10.26599/BDMA.2018.9020024
Open Access | Issue | Published: 02 July 2018

Understanding the Behavioral Differences Between American and German Users: A Data-Driven Study

Show Author's Information Chenxi Yang,  Yang Chen( ),  Qingyuan Gong,  Xinlei He,  Yu Xiao,  Yuhuan Huang,  Xiaoming Fu
School of Computer Science, Fudan University, Shanghai 200433, China, and the Engineering Research Center of Cyber Security Auditing and Monitoring, Ministry of Education, Shanghai 200433, China.
Department of Communications and Networking, Aalto University, 02150 Espoo, Finland.
Faculty of European Languages and Cultures, Guangdong University of Foreign Studies, Guangzhou 510420, China.
Institute of Computer Science, University of Göttingen, 37077 Göttingen, Germany.
Keywords:
machine learning, online social networks, behavioral difference, Yelp
Cite this article:
Yang C, Chen Y, Gong Q, et al. Understanding the Behavioral Differences Between American and German Users: A Data-Driven Study. Big Data Mining and Analytics, 2018, 1(4): 284-296. https://doi.org/10.26599/BDMA.2018.9020024
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Abstract Full text About this article

Given that the USA and Germany are the most populous countries in North America and Western Europe, understanding the behavioral differences between American and German users of online social networks is essential. In this work, we conduct a data-driven study based on the Yelp Open Dataset. We demonstrate the behavioral characteristics of both American and German users from different aspects, i.e., social connectivity, review styles, and spatiotemporal patterns. In addition, we construct a classification model to accurately recognize American and German users according to the behavioral data. Our model achieves high classification performance with an F1-score of 0.891 and AUC of 0.949.


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About this article

Understanding the Behavioral Differences Between American and German Users: A Data-Driven Study

Show Author's information Chenxi Yang, Yang Chen( ), Qingyuan Gong, Xinlei He, Yu Xiao, Yuhuan Huang, Xiaoming Fu
School of Computer Science, Fudan University, Shanghai 200433, China, and the Engineering Research Center of Cyber Security Auditing and Monitoring, Ministry of Education, Shanghai 200433, China.
Department of Communications and Networking, Aalto University, 02150 Espoo, Finland.
Faculty of European Languages and Cultures, Guangdong University of Foreign Studies, Guangzhou 510420, China.
Institute of Computer Science, University of Göttingen, 37077 Göttingen, Germany.

Abstract

Given that the USA and Germany are the most populous countries in North America and Western Europe, understanding the behavioral differences between American and German users of online social networks is essential. In this work, we conduct a data-driven study based on the Yelp Open Dataset. We demonstrate the behavioral characteristics of both American and German users from different aspects, i.e., social connectivity, review styles, and spatiotemporal patterns. In addition, we construct a classification model to accurately recognize American and German users according to the behavioral data. Our model achieves high classification performance with an F1-score of 0.891 and AUC of 0.949.

Keywords: machine learning, online social networks, behavioral difference, Yelp

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Publication history

Received: 05 March 2018
Accepted: 20 March 2018
Published: 02 July 2018
Issue date: December 2018

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© The author(s) 2018

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 61602122 and 71731004), the Natural Science Foundation of Shanghai (No. 16ZR1402200), Shanghai Pujiang Program (No. 16PJ1400700), EU FP7 IRSES MobileCloud project (No. 612212), and Lindemann Foundation (No. 12-2016).

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