Home > Lectures & Seminars > Empirical Analysis of Unstructured and Multi-Modal Data

Empirical Analysis of Unstructured and Multi-Modal Data

Thu, Jul 04, 2024




TENCENT:946 688 226



This talk presents three empirical papers on extracting new features from unstructured and multi-modal data and deriving new managerial insight. The first paper analyzes a million-scale dynamic multiplex network and shows new multiplex network are associated with volunteer crowdsourcing behaviors. The second paper analyzes text and picture data and shows multi-modal affective information is associated with review helpfulness. The third paper analyzes text, picture, and video data of outstream video ads and show multi-modal features are associated with consumer attention and clicking behaviors.


Dr. Yu obtained his PhD degree from University of Washington. He received bachelor’s and master’s degrees from Tsinghua University. His research centers on (1) the economics of artificial intelligence and machine learning; (2) business analytics leveraging online unstructured data (i.e., text, images, videos, networks, behavioral sequences). He has published in top journals in the area of Information Systems (ISR and MISQ) and received several best paper awards or nominations at the top Information Systems conferences (e.g., INFORMS, CIST, and WITS).



X Thank you for your interest in Master of Global Management, Tongji University!