Siqi Wu
Abstract
Siqi Wu is a postdoctoral research fellow in the Center for Social Media Responsibility at the University of Michigan (Ann Arbor). 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 crowd-sourcing 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 was also a recipient of the Google PhD fellowship. More information about Siqi's work can be found at https://avalanchesiqi.github.io
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.
- Kong, Q., Rizoiu, M.-A., Wu, S., and Xie, L. 2018. Will this video go viral: Explaining and predicting the popularity of YouTube videos. In Companion Proceedings of the The Web Conference.Google Scholar
- Krumme, C., Cebrian, M., Pickard, G., and Pentland, S. 2012. Quantifying social influence in an online cultural market. PloS One.Google Scholar
- Lee, J., Wu, S., Ertugrul, A. M., Lin, Y.-R., and Xie, L. 2022. Whose advantage? Measuring attention dynamics across YouTube and Twitter on controversial topics. In Proceedings of the 16th International AAAI Conference on Web and Social Media.Google Scholar
- Salganik, M. J., Dodds, P. S., and Watts, D. J. 2006. Experimental study of inequality and unpredictability in an artificial cultural market. Science.Google Scholar
- Shin, M., Tran, A., Wu, S., Mathews, A., Wang, R., Lyall, G., and Xie, L. 2021. AttentionFlow: Visualising influence in networks of time series. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining.Google Scholar
- Wu, S. and Resnick, P. 2021. Cross-partisan discussions on YouTube: Conservatives talk to liberals but liberals dont talk to conservatives. In Proceedings of the 15th International AAAAI Conference on Web and Social Media.Google Scholar
- Wu, S., Rizoiu, M.-A., and Xie, L. 2018. Beyond views: Measuring and predicting engagement in online videos. In Proceedings of the 12th International AAAI Conference on Web and Social Media.Google Scholar
- Wu, S., Rizoiu, M.-A., and Xie, L. 2019. Estimating attention flow in online video networks. Proceedings of the ACM on Human-Computer Interaction CSCW.Google Scholar
- Wu, S., Rizoiu, M.-A., and Xie, L. 2020. Variation across scales: Measurement fidelity under Twitter data sampling. In Proceedings of the 14th International AAAI Conference on Web and Social Media.Google Scholar
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