[gov3009-l] Applied Stats Workshop TOMORROW (Wed. 10/31) - Edward Kao and Steven Smith

*FINAL REMINDER --- Applied Statistics Workshop TOMORROW (10/31) at 12 noon* *Lunch provided --- All are welcome * ------------------------------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------------------------------ Dear workshop community, We will convene for the Applied Statistics Workshop (Gov 3009) tomorrow on Wednesday (10/31). The speakers are* Edward Kao *and* Steven Smith *(MIT Lincoln Laboratory) who will be presenting their work "Network Causal Inference on Social Media Influence Operations" (paper link <https://arxiv.org/abs/1804.04109>). *Where:* CGIS Knafel Building, Room K354 (see this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When: *Wednesday, October 31st --- 12 noon - 1:30 pm. *Abstract:* Estimating influence on social media networks is an important practical and theoretical problem, especially because this new medium is widely exploited as a platform for disinformation and propaganda. This paper introduces a novel approach to influence estimation on social media networks and applies it to the real-world problem of characterizing active influence operations on Twitter during the 2017 French presidential elections. The new influence estimation approach attributes impact by accounting for narrative propagation over the network using a network causal inference framework applied to data arising from graph sampling and filtering. This causal framework infers the difference in outcome as a function of exposure, in contrast to existing approaches that attribute impact to activity volume or topological features, which do not explicitly measure nor necessarily indicate actual network influence. Cramer-Rao estimation bounds are derived for parameter estimation as a step in the causal analysis, and used to achieve geometrical insight on the causal inference problem. The ability to infer high causal influence is demonstrated on real-world social media accounts that are later independently confirmed to be either directly affiliated or correlated with foreign influence operations using evidence supplied by the U.S. Congress and journalistic reports. *All are welcome! Lunch is provided! * Best, Connor Jerzak Applied Statistics Workshop -- Graduate Student Coordinator An anonymous feedback form for the workshop can be found here at this link <https://docs.google.com/forms/d/e/1FAIpQLScp4lPVBtp4Akf6K6ggmfcTUSIUHEJX89-CU8HWrQPpe9pjTw/viewform?usp=sf_link>. Workshop listserv sign-up at this link <https://lists.fas.harvard.edu/mailman/listinfo/gov3009-l>.