During the past decades,the term“social computing”has become a promising interdisciplinary area in the intersection of computer science and social science.In this work,we conduct a data-driven study to understand th...During the past decades,the term“social computing”has become a promising interdisciplinary area in the intersection of computer science and social science.In this work,we conduct a data-driven study to understand the development of social computing using the data collected from Digital Bibliography and Library Project(DBLP),a representative computer science bibliography website.We have observed a series of trends in the development of social computing,including the evolution of the number of publications,popular keywords,top venues,international collaborations,and research topics.Our findings will be helpful for researchers and practitioners working in relevant fields.展开更多
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 t...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.展开更多
基金supported by the National Natural Science Foundation of China(Nos.71731004,62072115,62102094,62173095,and 61602122)Shanghai Science and Technology Innovation Action Plan Project(No.22510713600)Natural Science Foundation of Shanghai(No.21ZR1404700).
文摘During the past decades,the term“social computing”has become a promising interdisciplinary area in the intersection of computer science and social science.In this work,we conduct a data-driven study to understand the development of social computing using the data collected from Digital Bibliography and Library Project(DBLP),a representative computer science bibliography website.We have observed a series of trends in the development of social computing,including the evolution of the number of publications,popular keywords,top venues,international collaborations,and research topics.Our findings will be helpful for researchers and practitioners working in relevant fields.
基金supported by the National Natural Science Foundation of China(Nos.61602122 and 71731004)the Natural Science Foundation of Shanghai(No.16ZR1402200)+2 种基金Shanghai Pujiang Program(No.16PJ1400700)EU FP7 IRSES Mobile Cloud project(No.612212)Lindemann Foundation(No.12-2016).
文摘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.
