Popularity Prediction for Social Media over Arbitrary Time Horizons
Daniel Haimovich, Dima Karamshuk, Thomas Leeper, Evgeniy Riabenko, Milan Vojnovic
Practice & Experience in Advanced Research Computing (PEARC)
With the increasing popularity of computational approaches to conduct social science research, building a scalable and efficient computing platform has become a topic of interest for academia to empower research labs and institutes to analyze large-scale data. While social science researchers have been very excited about the advancement of emerging technologies in big data, deep learning, computer vision, network analysis, etc., they are also constrained by the available computing resources to analyze data. This paper describes a scalable solution to deploy JupyterHub for computational social science research on the cloud. We use a reference architecture on AWS to walk through the design principles and details. Our architecture has helped facilitate several collaborations between Facebook and academia. The case study (Facebook Open Research and Transparency platform) shows that our architecture, using technologies like containerization and serverless computing, can support thousands of users to analyze web-scale datasets.
Daniel Haimovich, Dima Karamshuk, Thomas Leeper, Evgeniy Riabenko, Milan Vojnovic
Liqi Yan, Qifan Wang, Yiming Cu, Fuli Feng, Xiaojun Quan, Xiangyu Zhang, Dongfang Liu
Barlas Oğuz, Kushal Lakhotia, Anchit Gupta, Patrick Lewis, Vladimir Karpukhin, Aleksandra Piktus, Xilun Chen, Sebastian Riedel, Wen-tau Yih, Sonal Gupta, Yashar Mehdad