Shan Huang is an assistant professor at the Foster School of Business at the University of Washington, Seattle. Her research focuses on the digital economy, social networks, and business analytics. The goal of Shan’s current work is to investigate how new social media shapes the information environment and decision making that leads to non-negligible economic and social impacts. Specifically, her studies examine how social advertising and social referral affect product virality, how emotions shape online content diffusion, and how misinformation diffuses through weak ties in massive social networks. She has a particular interest in understanding how certain phenomena vary across individuals, social ties, products, and markets, using population-scale datasets and large-scale field tests, and uses various research methodologies (e.g., large-scale networked randomized field experiments, machine learning, network analysis, econometrics) to pursue her research agenda. Shan’s research has been published in prominent management journals, including Marketing Science and the Journal of Management Information Systems. She has been collaborating closely with the leading tech firms (e.g., Tencent) to understand cutting-edge digital phenomena and their implications for business and society. Shan Huang received a bachelor’s degree from Tsinghua University, a master’s degree from the University of British Columbia, and a Ph.D. from the MIT Sloan School of Management.
Journal Publications and Selected Working Papers
"Social Advertising Effectiveness across Products: A Large-scale Randomized Filed Experiment", Shan Huang, Sinan Aral, Yu Jeffrey Hu, Erik Brynjolfsson, Marketing Science.
“Monetary Incentive and Stock Opinions on Social Media ", Hailiang Chen, Yu Jeffrey Hu, Shan Huang Journal of Management Information Systems 36(2) 391-417.
"The Effectiveness of Social Advertising: Public and Private User Engagements", Shan Huang, Song Lin. R&R
"Emotions in Online Content Diffusion", Yifan Yu, Shan Huang, Yuchen Liu, Yong Tan. under review