Understanding Problematic Sharing Behavior on Facebook
Ohio State University
In an information environment where sharing decisions influence how billions of people around the world learn about science, politics, and their community, it is crucial that we understand how these decisions are made. Of particular concern are what we term “problematic sharing behaviors,” including sharing dubious news and falsehoods. We will pair Facebook data with time series data describing high-profile events and documented changes to the Facebook platform. We aim to produce two types of explanations of sharing behavior. The first will focus on temporal patterns. For example, it is likely that the proportion of “problematic” sharing will vary by day, month, or season. The second type of explanation concerns the influence that important social events and technological changes have on problematic sharing. High-profile crises, from natural disasters to acts of mass violence, are likely to lead to some forms of problematic sharing, while changes to Facebook are intended to constrain it.
R. Kelly Garrett
Associate Professor, Ohio State University
R. Kelly Garrett is an associate professor in the School of Communication at the Ohio State University. His research interests include the study of online political communication, online news, and the ways in which citizens and activists use new technologies to shape their engagement with contentious political topics. His most recent work, which was supported by a National Science Foundation CAREER Award, focuses on how people’s exposure to and perceptions of online political information are related to their political beliefs. His work has been published in journals such as PLOS One, the Journal of Communication, Communication Research, Political Behavior, and the Journal of Computer-Mediated Communication, among others. More information about his work is available at http://www.rkellygarrett.com.
Assistant Professor, Ohio State University
Robert M. Bond is an assistant professor in the School of Communication at Ohio State University. His core research interest is in political communication, behavior, and attitudes, specifically how our social networks influence our political behavior and communication practices. His work frequently uses computational methods to understand why people behave as they do, how they communicate, and what the effects of communication are for politics. Much of this core area of research uses big data to study social influence on political behaviors and attitudes, including large-scale field experiments on turnout and observational work on ideology. In addition to these main areas of research, he has studied the development of political attitudes and behaviors in the social networks of adolescents, social network effects on aggression, and social attitudes about prejudice, using social network techniques.
Assistant Professor, University of Michigan–Ann Arbor
Ceren Budak is an assistant professor of information and assistant professor of electrical engineering and computer science, College of Engineering at the University of Michigan. Dr. Budak's research interests lie in the area of computational social science, a discipline at the intersection of computer science, statistics, and the social sciences. She is interested in applying large-scale data analysis, machine learning, and network science techniques to study problems with social, political, and policy implications.
Assistant Professor, Stony Brook University
Jason Jones is an assistant professor in the Department of Sociology and the Institute for Advanced Computational Science at Stony Brook University. In his work, he takes advantage of massive datasets to re-examine what we think we know about human behavior. For example, he has collaborated with Facebook to conduct experiments regarding social norms around voting—each experiment generating data from millions of participants. In another collaboration, he and his colleagues re-imagined and redefined Granovetter's "strength of weak ties" hypothesis by taking into account the work histories and social networks of millions of people around the world. Broadly, his research interests involve the application of computational social science to predict political, health, and other social behaviors.
Assistant Professor, Cornell University
Drew Margolin is an assistant professor in the Department of Communication at Cornell University. His research focuses on macro-level influences on the production of discourse through the analysis of digital, observational data. His recent work focuses on the conditions conducive to effective fact-checking and the influence of large-scale events on the expression of emotions.