Cory Costello is a PhD candidate in psychology at the University of Oregon. He is a member of the Personality and Social Dynamics Lab (PI: Sanjay Srivastava). His research interests include Personality (who we are), Interpersonal Perception (what people think about us), Reputations (what people say and hear about us), how these are manifest in online environments, and the extent to which they’re recoverable from digital footprints. He combines survey, experimental, and data scientific methods in pursuit of these interests. He is an open science and R enthusiast. His methodological interests include social network analysis, predictive modelling / machine learning, text analysis, structural equation modelling, hierarchical linear modelling.
PhD in Personality/Social Psychology, 2020 (expected)
University of Oregon
MA in Psychology, 2014
Wake Forest University
BA in Psychology, 2012
New College of Florida (The State's Honors College)
A Naturalistic, laboratory paradigm for studying the spread of reputational information.
Work examining longitudinal personality change and stability in the life and time project
The past decade has seen rapid growth in research linking stable psychological characteristics (i.e., traits) to digital records of online behavior in Online Social Networks (OSNs) like Facebook and Twitter, which has implications for basic and applied behavioral sciences. Findings indicate that a broad range of psychological characteristics can be predicted from various behavioral residue online, including language used in posts on Facebook (Park et al., 2015) and Twitter (Reece et al., 2017), and which pages a person ‘likes’ on Facebook (e.g., Kosinski, Stillwell, & Graepel, 2013). The present proposal seeks to examine the extent to which the accounts a user follows on Twitter – their Twitter friends – can predict individual differences in self-reported anxiety, depression, post-traumatic stress, and anger. Studying Twitter friends offers distinct theoretical and practical advantages for researchers, including the potential for less overt impression management and better capturing passive users. By incorporating best practices in open science and machine learning, we aim to provide unbiased estimates of predictive accuracy for predicting Mental Health from Twitter friends. Our findings will have implications for theories linking psychological traits to behavior online, applications seeking to infer psychological characteristics from records of online behavior, and for informing discussions of how such applications could affect users’ privacy.
In a large, nationally-representitive sample, we find mean-level change toward more inclusive value priorities and high rank-order stability of values over time.