Siqi Wu is a postdoctoral research fellow in the Center for Social Media Responsibility at the University of Michigan. Prior to that, he was a research fellow in the Computational Media Lab at the Australian National University, where he also completed his Ph.D. (Computer Science). His research interests include computational social science, social computing, and crowdsourcing systems. He has published papers at ICWSM, CSCW, CIKM, WWW, and WSDM. He has received one best paper honorable mention award at CSCW and one best paper finalist award at ICWSM. He is also a recipient of the Google PhD fellowship.
In his thesis, Siqi focused on understanding how online content captures collective human attention. He tackled a series of questions, including (a) how does Twitter API's sampling mechanism impact common measurements? (b) why do some YouTube videos keep the users staying longer? (c) how does YouTube recommender system drive user attention? (d) how do liberals and conservatives engage in cross-partisan discussions online? and (e) how does online attention transcend across platforms, across topics, and over time? Altogether, his research explores the collective consumption patterns of human attention in digital platforms. Methods, observations, and software demonstrations from his work can be used by content owners, hosting sites, and online users alike to improve video production, recommender systems, and advertising strategies.
Measuring Collective Attention in Online Content: Sampling, Engagement, and Network Effects
Fields of interest
Computational Social Science
Social Media and Social Computing
Science, Technology, and Society
B.E. in Electronics Engineering, Tianjin University, 2012
M.S. in Information Technology, University of Melbourne, 2015
Ph.D. in Computer Science, Australian National University, 2021
Wu, S. and Resnick, P., 2021. Cross-Partisan Discussions on YouTube: Conservatives Talk to Liberals but Liberals Don't Talk to Conservatives. In Proceedings of the Fifteenth International AAAI Conference on Web and Social Media. [Best Paper Finalist Award]
Wu, S., Rizoiu, M.A. and Xie, L., 2019. Estimating Attention Flow in Online Video Networks. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW). [Best Paper Honorable Mention Award]
Wu, S., Rizoiu, M.A. and Xie, L., 2018. Beyond Views: Measuring and Predicting Engagement in Online Videos. In Proceedings of the Twelfth International AAAI Conference on Web and Social Media.