嘉　宾：Yilin Li  Postdoctoral Fellow 北京大学

主持人：房思含 助理教授 上海交通大学安泰经济与管理学院

时　 间：2025年12月16日（周二）13:30-15:00

地　 点：上海交通大学徐汇校区安泰楼A305

内容简介:

This study addresses the challenge digital content creators face in harnessing the vast and fragmented information from consumers on social media platforms. We reevaluate the weak tie theory in the context of information overload and introduce the concept of two-sided closure, a network structure that enhances creators‘ ability to manage and utilize extensive information through networks that include content creators and consumers. Using large-scale data from a major social media platform and a quasi-experimental design leveraging creators‘ entry into peer groups with staggered difference-in-differences, we find that two-sided closure raises content novelty and expands the effective information- processing frontier—creators with denser two-sided closure produce more novel content even at comparable levels of audience exposure. Theoretically, we add an underexplored perspective: network structures differ in their capacity to leverage varying information volume, refining predictions under information abundance and specifying limits to weak-tie advantages. Practically, we highlight networks as a governable substrate for both social media creators and platform managers: orchestrating cross-side ties and peer coordination around shared-audience signals can improve information utilization and stimulate novel content production.

演讲人简介：

Yilin Li is a Postdoctoral Fellow at the Guanghua School of Management, Peking University. She received her Ph.D. in Management Science and Engineering from the School of Economics and Management at Tsinghua University in 2022.

Dr. Li's research is situated at the intersection of artificial intelligence, quantitative analysis, and real-world managerial challenges. Her primary interests include social networks and user innovation, recommendation systems and algorithmic mechanisms, and human–AI collaboration and feedback systems. In her work, she integrates methodologies such as deep learning, user behavior analysis, and explainable AI to develop novel solutions. Furthermore, her algorithmic work in social network analysis has resulted in an authorized invention patent. She is also actively exploring the application of large language models (LLMs) combined with graph neural network algorithms for business text evaluation tasks.

Her research has been published in journals such as MIS Quarterly and Decision Support Systems. She also has manuscripts under major revision or review at other top-tier journals, including MIS Quarterly, Information Systems Research, and Administrative Science Quarterly.

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