Estimating Multiple Socioeconomic Attributes via Home Location-A Case Study in China

作     者：Shichang Ding Xin Gao Yufan Dong Yiwei Tong Xiaoming Fu 

作者机构：State Key Laboratory of Mathematical Engineering and Advanced ComputingZhengzhou 276800China Department of SociologyTsinghua UniversityBeijing 100085China Institute of Computer ScienceUniversity of GottingenGottingen 37077Germany Shanghai Hejin Information Technology CompanyShanghai 200100China 

出 版 物：《Journal of Social Computing》 (社会计算（英文）)

年 卷 期：2021年第2卷第1期

页      面：71-88页

核心收录：

学科分类：0502[文学-外国语言文学] 050201[文学-英语语言文学] 05[文学] 0833[工学-城乡规划学] 

基　　金：The research work was partly funded by the European Union’s Horizon 2020 Research and Innovation Program under the Marie Sklodowska-Curie(No.824019) the Tsinghua-Gottingen Student Exchange Project(No.IDSSSP-2017001) 

主　　题：personal income family income occupation education multi-task learning 

摘      要：Inferring people’s Socioeconomic Attributes(SEAs),including income,occupation,and education level,is an important problem for both social sciences and many networked applications like targeted advertising and personalized *** works mainly focus on estimating SEAs from peoples’cyberspace behaviors and relationships,such as the content of tweets or the social networks between online *** cyberspace data,alternative data sources about users’physical behavior,like their home location,may offer new *** specifically,in this paper,we study how to predict a person’s income level,family income level,occupation type,and education level from his/her home *** a case study,we collect people’s home locations and socioeconomic attributes through a survey involving 9 provinces and 85 cities in *** further enrich home location with the knowledge from real estate websites,government statistics websites,online map services,*** learn a shared representation from input features as well as attribute-specific representations for different SEAs,we propose H2SEA,a factorization machine-based multi-task learning method with attention *** experiment results show that:(1)Home location can clearly improve the estimation accuracy for all SEA prediction tasks(e.g.,80.2%improvement in terms of F1-score in estimating personal income level);(2)The proposed H2SEA model outperforms alternative models for SEA inference in terms of various evaluation metrics,such as Area Under Curve(AUC),F-measure,and specificity;(3)The performance of specific SEA prediction tasks(e.g.,personal income)can be further improved if H2SEA only focuses on cities or villages due to urban-rural gap in China;(4)Compared with online crawled housing price data,the area-level average income and Points Of Interest(POI)are more important features for SEA inferences in China.