Information extraction from digital social trace data with applications to social media and scholarly communication data

Abstract

Shubhanshu is a Machine Learning Researcher at Twitter working on the Content Understanding Research team. He finished his Ph.D. at the iSchool, University of Illinois at Urbana-Champaign, where he worked as a research assistant with Dr. Jana Diesner and Dr. Vetle Torvik on projects funded by NIH, NSF, KISTI, and Army Research Lab. Shubhanshu's research work is at the intersection of machine learning, information extraction, social network analysis, and visualizations. His work won then Best Student Paper Award at ASIS&T SIGMET workshop. Shubhanshu has published multiple open source projects as well as open datasets related to his research. More information about Shubhanshu can be found at: https://shubhanshu.com. In his thesis, Shubhanshu worked on unifying the concept of information extraction from social media data and scholarly data under the framework of Digital Social Trace Data (DSTD). This framework allow us to answer questions like conceptual novelty, expertise in scientific communities as well as influential users in social media corpus. He further proposed improved information extraction techniques for social media text by using active, semi-supervised, and multi-task machine learning along with the inclusion of meta-data. Additionally, his work resulted in the development of a visualization framework for DSTD along with multiple open source tools and datasets. For more details visit https://shubhanshu.com/phd_thesis/.