Center for Informed Democracy & Social - cybersecurity (IDeaS)

Daniele Bellutta was raised in both the Los Angeles area and the Trentino province of Italy, where he was born. He holds bachelor's degrees in computer science and political science from The Ohio State University as well as a master's degree in computer science from Dartmouth College. He has interned at such places as NASA's Jet Propulsion Laboratory and has conducted research in diverse fields, including radar mapping and terrorist network analysis. Currently, he is a doctoral student in the societal computing program in the School of Computer Science at Carnegie Mellon University, where his research applies network science to topics such as online disinformation.

Description of Research:

Daniele Bellutta's recent research has focused on assessing the influence of bots in Twitter conversations about national elections. His project for the fellowship seeks to model user actions as a vehicle for measuring the effects of online misinformation on user behavior. Such research strives to fill in the gap of knowledge surrounding misinformation's actual impact on people.

Mason Broxham is from Elgin, Illinois and enjoys debates about whether Pittsburgh is a part of the mid-west. He is a 4th year PhD student in the Philosophy Department where he studies philosophy of language. Prior to attending CMU, Mason completed an MA in philosophy at Northern Illinois University, where he worked on theories of truth, metaethics, and political philosophy. Prior to this he completed his BA in economics at Augustana College.

Description of Research:

Mason's interests are in philosophy of language and social epistemology with particular emphasis on representations of shared information, how we communicate information to one another, how that information is structured, and what affects our ability to access and reason with it. He wants to better understand what factors contribute to polarization in online information spaces. Mason is working on modeling the effects of content moderation with evolutionary game theory.

Catherine King is a Ph.D. student in Societal Computing at Carnegie Mellon University advised by Dr. Kathleen Carley. Before coming to CMU, she earned a BS in Mathematics and an MS in Computational Operations Research at The College of William and Mary. She worked for a few years in industry before deciding to attend CMU to obtain her Ph.D. While her research interests have changed over time, she has always been interested in understanding technology's impact on society and applying her modeling skills to real-world systems.  

Thomas Magelinski is a fourth-year PhD. Student in Societal Computing advised by Professor Kathleen Carley. His research falls at the intersection of Network Science, Dynamical Systems, and Machine Learning. Specifically, his work seeks to leverage network dynamics to better understand social media discourse. He received an ARCS foundation scholarship in 2017 to support this work. He obtained the Honors Baccalaureate Degree in Engineering Science and Mechanics from Virginia Tech in 2017, with minors in Math and Physics. His undergraduate thesis focused on computational models of military air drop systems.

Description of Research:

Tom is currently working on developing and applying dynamic community detection methods for multipartite networks to Twitter discourse. The goal is to find and characterize communities propagating misleading information. Specifically, he is the investigating the spread of untrustworthy news articles in the COVID-19 discussion, and the Reopen America campaign.

Isabel Murdock is a Ph.D. student in Electrical and Computing Engineering at Carnegie Mellon University and is advised by Dr. Kathleen M. Carley and Dr. Osman Yağan. She received her B.S. and M.S. degrees in Electrical and Computer Engineering from CMU, while completing Air Force ROTC at the University of Pittsburgh. She has interned at the Software Engineering Institute and National Reconnaissance Office, and enjoys volunteering with STEM-focused outreach programs for local middle and high school students.

Description of Research:

Isabel's research focuses on the multi-platform spread of information over social media. Specifically, she is interested in building models that accurately reflect how information propagates between social media platforms and analyzing the potential impact of different intervention strategies on the spread of misinformation.  

Chris Rodriguez is a PhD student in Behavioral Marketing & Decision Research at Carnegie Mellon University in the Tepper School of Management and the Department of Social and Decision Sciences where he is advised by Danny Oppenheimer. His interests revolve around how people make judgements and decisions in a variety of contexts, from everyday life to large financial decisions. Prior to this, he received his BA in Economics from Yale University.

Description of Research:

Chris' current research aims to develop better bot detection techniques. Bots can propagate information of dubious reliability and contaminate data in online social science research. With the advancement of bots, more and more bots are able to pass standard detection methodologies. With an approach grounded in psychology, Chris aspires to design better procedures to differentiate between humans and bot.

Jeremy Straughter is a Ph.D. student in Societal Computing at Carnegie Mellon University, advised by Dr. Kathleen Carley. He is a commissioned officer in the United States Army where he works in the field of operations research. His research intersects the fields of public policy, network science, and computer science.

Description of Research:

Jeremy’s research leverages network science and computational methods to address complex policy issues. His current research considers efficient methods for measuring the policy burden imposed by government entities and detecting overlapping communities of government regulations. His research also considers the implications of government’s strategic communications on social cybersecurity theory and practice.

Joshua Uyheng is a third-year PhD student in societal computing at Carnegie Mellon University. He is advised by Dr. Kathleen Carley at the Center for Computational Analysis of Social and Organizational Systems (CASOS). His research combines machine learning, network science, and social and cognitive psychology to examine the large-scale dynamics of hate speech and disinformation on social media platforms. He is especially interested in comparative work focused on the Asia-Pacific and the Global South. Previously, he has conducted research on human-in-the-loop modelling and computational social systems at Heidelberg University and the Nara Institute of Science and Technology. He holds an MS in societal computing from Carnegie Mellon University, and undergraduate degrees in mathematics and psychology from the Ateneo de Manila University. He hails from the Philippines.

Description of Research:

Josh’s current research uses social identity theory and constructural theory to conceptualize online hate speech in terms of the narrative and network dynamics of groups. His work develops computational methods which characterize the prevalence and targets of hate speech, the properties of hateful communities, and how these processes are exploited by bots and trolls in the context of information operations. He specifically investigates these issues across diverse online conversations around national elections, natural disasters, social unrest, and the COVID-19 pandemic. Through his research, he aims to expand global and interdisciplinary understandings of the problem of hate speech, and ultimately illuminate new directions for making digital platforms more resilient to violence and conflict.