题目：Trust Mining in Online Social Networks: Trust Formation and Trust Prediction

报告人：赵惠民 教授

时间：2018年6月25日下午2:30-4:30

地点：南校区信远楼II区324（伟德国际BETVlCTOR报告厅）

Huimin Zhao is a Professor of Information Technology Management at the Lubar School of Business, University of Wisconsin-Milwaukee. He received the B.E. and M.E. degrees in Automation from Tsinghua University and the Ph.D. degree in Management Information Systems from the University of Arizona. His current research interests include data mining and healthcare informatics. He has published in such journals as MIS Quarterly, Communications of the ACM, ACM Transactions on MIS, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Systems, Man, and Cybernetics, Information Systems, Journal of Management Information Systems, Journal of the AIS,and Decision Support Systems. He currently serves as a senior editor for Decision Support Systems, associate editor for Information Systems Research, and associate editor for the Journal of Business Analytics, and has served in the past as an associate editor for MIS Quarterly. He has served as a co-chair of the 19thWorkshopon Information Technologies and Systems (WITS), the 5th INFORMS Workshop on Data Mining and Health Informatics, and the 9th China Summer Workshop on Information Management (CSWIM).

邀请人：赵捧未教授、秦春秀副教授（伟德国际BETVlCTOR/陕西信息资源研究中心）

报告内容简介：

Trust plays a critical role in online social relationships, because of the high levels of risk and uncertainty involved. Guided by relevant social science and computational graph theories, we develop conceptual and predictive models to gain insights into trusting behaviors in online social relationships.

First, we propose a conceptual model of trust formation in online social networks. We integrate the existing graph-based view of trust formation in social networks with socio-psychological theories of trust to provide a richer understanding of trusting behaviors in online social networks. We introduce new behavioral antecedents of trusting behaviors and redefine and integrate existing graph-based concepts to develop the proposed conceptual model. Our empirical findings indicate that both socio-psychological and graph-based trust-related factors should be considered in studying trust formation in online social networks.

Second, we propose a theory-based predictive model to predict trust and distrust links in online social networks. Previous trust prediction models used limited network structural data to predict future trust/distrust relationships, ignoring the underlying behavioral trust-inducing factors. We identify a comprehensive set of behavioral and structural predictors of trust/distrust links based on related theories, and then build multiple supervised classification models to predict trust/distrust links in online social networks. Our empirical results confirm the superior predictive performance of the proposed model over several baselines.