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November 8 1:00 pm - 2:00 pm EST
Title: Maximizing and Moderating Opinions in Social Networks
Speaker: Panayiotis Tsaparas, University of Ioannina, Greece
Location: 11th Floor, 177 Huntington Avenue, Boston, MA 02115, United States
The process of opinion formation through synthesis and contrast of different viewpoints has been the subject of many studies in economics and social sciences. Today, online social networks and social media have become the primary forum for people to create relationships, express opinions, and engage in discussions and debates. This has enabled the systematic analysis of opinion dynamics at a global scale, and has raised new research challenges. In our work, we adopt a well-established model for social opinion dynamics, and we study the following problems: (1) The Campaign problem, where the goal is to identify a set of target individuals whose positive opinion will maximize the overall positive opinion in the social network. (2) The Moderate problem, where the goal is to identify a set of target individuals whose moderate opinion will minimize the polarization in the network. In the course of our work, we uncover an interesting connection between the opinion formation process and random walks with absorbing nodes, and we propose a novel metric for measuring polarization in social networks.
About the Speaker
Panayiotis Tsaparas completed his undergraduate studies at Computer Science Department of University of Crete, Greece in 1995. He continued his graduate studies at University of Toronto, where he received his M.Sc., and Ph.D degree, under the supervision of Allan Borodin. After graduation, he worked as a post-doctoral fellow at University of Rome, “La Sapienza”, as a researcher at University of Helsinki, and as a researcher at Microsoft Research. Since 2011 he joined the Department of Computer Science and Engineering of University of Ioannina, where he is now an Associate Professor. His research interests include Social Network Analysis, Algorithmic Data Mining, Web Mining and Information Retrieval.