Description:

Speaker:

Tauhid Zaman, Associate Professor of Operations Management, Yale School of Management

Title:

Detecting Bots and Assessing Their Impact in Social Networks

Abstract:

Online social networks are often subject to influence campaigns by malicious actors through the use of automated accounts known as bots.   We consider the problem of detecting bots in online social networks and assessing their impact on the opinions of individuals.  We begin by developing a bot detection algorithm based on the Ising model from statistical physics.  This algorithm can simultaneously identify multiple bots based on their network interaction patterns.  Our Ising model algorithm can identify bots with higher accuracy while utilizing much less data than other state of the art methods.  
   
We then develop a function we call generalized harmonic influence centrality to estimate the impact bots have on the opinions of users in social networks.  This function is based on a generalized opinion dynamics model which subsumes many other models.  We prove that this model reaches an equilibrium characterized by a set of linear equations.  By combining a neural network for measuring sentiment with the observed social network structure, we are able to calculate the generalized harmonic influence centrality of bots in multiple real social networks involving the 2016 US presidential election, Brexit, and the Gilets Jaunes protests in France.  For some networks we find that a limited number of bots can cause non-trivial shifts in the population opinions.   In other networks, we find that the bots have little impact.  Overall we find that generalized harmonic influence centrality is a useful operational tool to measure the impact of bots in social networks.

Speaker Bio:

Tauhid Zaman is an Associate Professor of Operations Management at the Yale School of Management. He received his BS, MEng, and PhD degrees in electrical engineering and computer science from MIT.  His research focuses on solving operational problems involving social network data using probabilistic models, network algorithms, and modern statistical methods.  Some of the topics he studies in the social networks space include combating online extremists and assessing the impact of bots.  His broader interests cover data driven approaches to investing in startup companies, algorithmic sports betting, and biometric data.  His work has been featured in the Wall Street Journal, Wired, Mashable, the LA Times, and Time Magazine.

Webinar:

Zoom link: https://northwestern.zoom.us/j/92654679478
Passcode: nico

About the Speaker Series:

Wednesdays@NICO is a vibrant weekly seminar series focusing broadly on the topics of complex systems and data science. It brings together attendees ranging from graduate students to senior faculty who span all of the schools across Northwestern, from applied math to sociology to biology and every discipline in-between. Please visit: https://bit.ly/WedatNICO for information on future speakers.