Understanding collective decision making at a large-scale, and elucidating how community organization and community dynamics shape collective behavior are at the heart of social science research. Communities are multi-faceted, complex and dynamic. In this talk I will present two approaches for learning community representations: a generic representation that could be used as an exploratory tool to find nuanced similarities between communities, and a task oriented representation. Both representations combine multiple types of signals - textual and contextual, e.g., the (social) network structure and community dynamics. I will show how this multifaceted model can accurately predict large-scale collective decision-making in a distributed environment. We demonstrate the applicability of our model through Reddit's r/place - a large-scale online experiment in which millions of users, self-organized in thousands of communities, clashed and collaborated in an effort to realize their agenda.
Dr. Oren Tsur is an Assistant Professor (Senior Lecturer) at the Department of Software and Information Systems Engineering at Ben Gurion University in Israel where he heads the NLP and Social Dynamics Lab (NASLAB), and the newly founded interdisciplinary Research Center for Digital Politics and Strategy (DPS@BGU). His work combines Machine Learning, Natural Language Processing (NLP), Social Dynamics, and Complex Networks. Specifically, Oren’s work varies from sentiment analysis to modeling speakers’ language preferences, hates-speech detection, community dynamics, and adversarial influence campaigns.