October 2, 2014: Seminar - Aram Galstyan: "Deciphering Social Interactions from Text"

Seminar Announcement

Deciphering Social Interactions from Text

Dr. Aram Galstyan
USC - Information Sciences Institute
Thursday, October 2, 2014
11:00 a.m., 636 SEO Building


Studies of social systems have traditionally focused on analyzing various structural properties of networks induced by social communication, while ignoring the content of communication. Despite recent advances, language-based analysis of social processes is still a challenging problem due to the lack of sound mathematical frameworks and adequate computational methods for extracting and analyzing useful social signals from unstructured text. Here I will describe our recent work on content-based analysis of social interactions, which involves two main steps: (a) Embedding communication content in an abstract content space, so that a sequence of textual exchanges is represented as trajectories in this space; and (b) Applying tools from information theory and dynamical systems to discover and characterize predictive signals from those trajectories. I will briefly describe the main elements of the technical approach, and demonstrate the usefulness of the proposed framework on two case studies: content-based characterization of social influence, and stylistic coordination indialogues.


Aram Galstyan is Research Assistant Professor at the USC Computer Science Department, and a Project Leader at the USC Information Sciences Institute. Dr. Galstyan's current research focuses on characterizing and predicting behavior of dynamic networks using information?theoretic concepts. His other research interests include developing statistical?physics based approaches for understanding the stability and robustness of various statistical inference algorithms. Dr. Galstyan holds a PhD in theoretical physics from the University of Utah (2000).

Host: Dr. Tanya Berger-Wolf

Copyright 2016 The Board of Trustees
of the University of Illinois.webmaster@cs.uic.edu
Helping Women Faculty Advance
Funded by NSF