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

Understanding the Behavioral Differences Between American and German Users: A Data-Driven Study
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摘要 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. 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.
出处 《Big Data Mining and Analytics》 2018年第4期284-296,共13页 大数据挖掘与分析(英文)
基金 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 Mobile Cloud project(No.612212) Lindemann Foundation(No.12-2016).
关键词 BEHAVIORAL DIFFERENCE online social networks Yelp machine learning behavioral difference online social networks Yelp machine learning
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