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

Chenxi Yang; Yang Chen; Qingyuan Gong; Xinlei He; Yu Xiao; Yuhuan Huang; Xiaoming Fu

doi:10.26599/BDMA.2018.9020024

Big Data Mining and Analytics (Dec 2018)

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

Chenxi Yang,
Yang Chen,
Qingyuan Gong,
Xinlei He,
Yu Xiao,
Yuhuan Huang,
Xiaoming Fu

Affiliations

Chenxi Yang: <institution content-type="dept">School of Computer Science</institution>, <institution>Fudan University</institution>, <city>Shanghai</city> <postal-code>200433</postal-code>, <country>China</country>, and the <institution content-type="dept">Engineering Research Center of Cyber Security Auditing and Monitoring</institution>, <institution>Ministry of Education</institution>, <city>Shanghai </city><postal-code>200433</postal-code>, <country>China</country>.
Yang Chen: <institution content-type="dept">School of Computer Science</institution>, <institution>Fudan University</institution>, <city>Shanghai</city> <postal-code>200433</postal-code>, <country>China</country>, and the <institution content-type="dept">Engineering Research Center of Cyber Security Auditing and Monitoring</institution>, <institution>Ministry of Education</institution>, <city>Shanghai </city><postal-code>200433</postal-code>, <country>China</country>.
Qingyuan Gong: <institution content-type="dept">School of Computer Science</institution>, <institution>Fudan University</institution>, <city>Shanghai</city> <postal-code>200433</postal-code>, <country>China</country>, and the <institution content-type="dept">Engineering Research Center of Cyber Security Auditing and Monitoring</institution>, <institution>Ministry of Education</institution>, <city>Shanghai </city><postal-code>200433</postal-code>, <country>China</country>.
Xinlei He: <institution content-type="dept">School of Computer Science</institution>, <institution>Fudan University</institution>, <city>Shanghai</city> <postal-code>200433</postal-code>, <country>China</country>, and the <institution content-type="dept">Engineering Research Center of Cyber Security Auditing and Monitoring</institution>, <institution>Ministry of Education</institution>, <city>Shanghai </city><postal-code>200433</postal-code>, <country>China</country>.
Yu Xiao: <institution content-type="dept">Department of Communications and Networking</institution>, <institution>Aalto University</institution>, <postal-code>02150</postal-code> <city>Espoo</city>, <country>Finland</country>.
Yuhuan Huang: <institution content-type="dept">Faculty of European Languages and Cultures</institution>, <institution>Guangdong University of Foreign Studies</institution>, <city>Guangzhou</city> <postal-code>510420</postal-code>, <country>China</country>.
Xiaoming Fu: <institution content-type="dept">Institute of Computer Science</institution>, <institution>University of Göttingen</institution>, <postal-code>37077</postal-code> <city>Göttingen</city>, <country>Germany</country>.

DOI: https://doi.org/10.26599/BDMA.2018.9020024
Journal volume & issue: Vol. 1, no. 4
pp. 284 – 296

Abstract

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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.

Published in Big Data Mining and Analytics

ISSN: 2096-0654 (Print)
Publisher: Tsinghua University Press
Country of publisher: China
LCC subjects: Science: Mathematics: Instruments and machines: Electronic computers. Computer science
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8254253

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