Predicting Mental Health from Followed Accounts 1 Running Head: Predicting Mental Health from Followed Accounts Predicting Mental Health from Followed Accounts on Twitter Cory K. Costello, Sanjay Srivastava, Reza Rejaie, & Maureen Zalewski University of Oregon Author note: Cory K. Costello, Sanjay Srivastava, and Maureen Zalewski, University of Oregon, Department of Psychology, 1227 University of Oregon, Eugene, OR, 97403. Reza Rejaie, University of Oregon, Department of Computer and Information Science, 1202 University of Oregon, Eugene, OR, 97403. Correspondence concerning this article should be addressed to Cory K. Costello, who is now at the Department of Psychology, University of Michigan, 530 Church Street, University of Michigan, Ann Arbor, MI 48109, email:
[email protected] Study materials, analysis code, and registrations (including pre-registered Stage 1 protocol) can be found at the project site: https://osf.io/54qdm/ This material is based on work supported by the National Institute of Mental Health under Grant # 1 R21 MH106879-01 and the National Science Foundation under NSF GRANT# 1551817. Predicting Mental Health from Followed Accounts 2 Abstract 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).