Statistically Principled and Scalable Computational Tools for Transforming Research

PI: Lei Guo (Emerging Media Studies, COM)
Co-PIs: Prakash Ishwar (Electrical & Computer Engineering, ENG), Margrit Betke (Computer Science, CAS)
Collaborators: Jacob Groshek (Emerging Media Studies, COM), Dino Christenson (Political Science, CAS)

This research is part of an ongoing project that is exploring reliable and valid methods to analyze large-scale social data in the context of communication research. Specifically, the study seeks to examine the efficacy and validity of using machine learning techniques for detecting topics in tweets and YouTube videos to be collected during the upcoming 2016 U.S. presidential election. Researchers aim to develop a comprehensive and replicable data-analytics framework that provides a timely analysis of the social media conversation about the 2016 election, which is believe by many to be the most important election of our lifetime.

This work is funded by a Hariri Research Award made in June, 2016.