Speaker: Senior Staff, Artificial Intelligence Software Architectures and Algorithms Group, MIT Lincoln Laboratory
Zoom: https://ucf.zoom.us/j/97111647289
Or watch online via Youtube at seminar.findingthefanatic.com
Title: Reconnaissance of Influence Operations
Abstract: This talk presents the causal inference approach used as part of an end to-end framework to automate detection of disinformation narratives, networks and influential actors. The framework integrates natural language processing, machine learning, graph analytics, and a network causal inference approach. The impact of each account is inferred by its causal contribution to narrative propagation over the network. It accounts for social confounders (e.g., community membership, popularity) and disentangles their effects using an approach based on the network potential outcome framework. Because it is impossible to observe both the realized and the counterfactual outcomes, the missing potential outcomes must be estimated, which is accomplished using a model. We demonstrate this approach's capability on real-world hostile IO campaigns and show that it correctly classifies known IO accounts and networks and discovers high-impact accounts that escape the lens of traditional impact statistics based on activity counts and network centrality. Results are corroborated with known IO accounts based on US Congressional reports, investigative journalism, and IO datasets provided by Twitter.
Bio: Dr. Steven Thomas Smith is a senior staff member in the Artificial Intelligence Software Architectures and Algorithms Group at MIT Lincoln Laboratory. He is an expert in radar, sonar, and signal processing who has made pioneering and wide-ranging contributions through his research and technical leadership in estimation theory, resolution limits, and signal processing and optimization on manifolds. He has more than 20 years of experience as an innovative technology leader in statistical data analytics, both theory and practice, and broad leadership experience ranging from first-of-a-kind algorithm development for groundbreaking sensor systems to graph-based intelligence architectures. Dr. Smith received the SIAM Outstanding Paper Award in 2001 and the IEEE Signal Processing Society Best Paper Award in 2010. He was associate editor of the IEEE Transactions on Signal Processing from 2000 to 2002 and serves on the IEEE Sensor Array and Multichannel and Big Data committees. Dr. Smith holds the BASc degree (1986) in electrical engineering and honours mathematics from the University of British Columbia, Vancouver, B.C., and the PhD degree (1993) in applied mathematics from Harvard University, Cambridge, Massachusetts.
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