Position Title
Graduate Student
Education
- University of California, Davis Davis, CA
- Doctor of Philosophy in Communication, 2019-2024
- PI: Dr. Richard Huskey
- University of California, Davis Davis, CA
- Master of Science in Statistics (Data Science Track), 2019-2023
- University of Illinois at Urbana-Champaign Urbana, IL
- Master of Science in Advertising, Aug 2019
- Advisor: Dr. Brittany Duff
- Zhejiang Gongshang University Hangzhou, China
- Bachelor of Art in Advertising, June 2017
About
I am Xuanjun (Jason) Gong. I am a Ph.D. candidate at Department of Communication, UC, Davis. I am a researcher at Cognitive Communication Science Lab, PI: Richard Huskey.
I am currently seeking for academic positions in the field of communication this year.
My research has been published in outlets such as Journal of Communication, Human Communication Research, Computational Communication Research, American Behavioral Scientist, and Journal of Medical Internet Research.
Research Focus
media selection, computational modeling, communication networks, and information diffusion
Publications
Gong, X., Huskey, R., Xue, H., Shen, C., & Frey, S. (2023). Broadcast information diffusion processes on social media networks: exogenous events lead to more integrated public discourse. Journal of Communication, 2023;, jqad014.
Gong, X., Huskey, R., Eden, A. & Ulusoy, E. (in press) People prefer negatively-valenced movies in a two-alternative movie decision task: A drift-diffusion modeling approach for testing mood management theory. Journal of Communication.
Gong, X. & Huskey, R. (in press). Moving behavioral experimentation online: A tutorial and some recommendations for drift diffusion modeling. American Behavioral Scientist.
Gong, X., & Huskey, R. (conditional acceptance). Computational Modeling Entertainment Media Choice and Decision Making in Communication Science. In Bowman, N. D. (Ed.), DeGruyter Handbook of Entertainment. (Volume 1.). Berlin, Germany: DeGruyter.
Gong, X., & Huskey, R. (working paper). Media selection is highly predictable, In principle. Computational Communication Research. https://computationalcommunication.org/ccr/preprint
Huskey, R., Keene, J. R., Wilcox, S., Gong, X., Adams, R., & Najera, C. J. (2022). Flexible and modular brain network dynamics characterize flow experiences during media use: A functional magnetic resonance imaging study. Journal of Communication, 72(1), 6-32
Xue, H., Gong, X., & Stevens, H. (2022). COVID-19 Vaccine Fact-Checking Posts on Facebook: Observational Study. Journal of Medical Internet Research, 24(6), e38423.
Teaching
Instructor:
CMN 001: Introduction to Public Speaking
Teaching Assistant:
CMN 140: Mass Communication CMN 120: Interpersonal Communication CMN 110: Communication Networks
CMN 12Y: Data Visualization in Social Science
Awards
Top Paper Award (2023) - International Communication Association - Communication Science and Biology Annual Meeting of the International Communication Association
Gong, X. & Huskey, R. (2023). Media selection is highly predictable, In principle.
Top Paper Award (2021) - National Communication Association Annual Conference Annual Meeting of the National Communication Association
Gong, X. & Huskey, R. (2021). People Prefer Negatively-Valenced Movies in a Two-Alternative Movie Decision Task: A Drift Diffusion Modeling Approach for Testing Mood Management Theory.
Graduate Student Reward (2021) - Cognitive Neuroscience Society Annual Meeting of the Cognitive Neuroscience Society
Gong, X. & Huskey, R. (March, 2021). Fronto-Parietal and Reward Networks are Integrated During the Psychological State of Flow. Annual Meeting of the Cognitive Neuroscience Society, Virtual Conference.
Top 5 Paper award (2021) - International Communication Association Annual Meeting of the International Communication Association
Huskey, R., Keene, J., Wilcox, S., Gong X., Adams, R. & Najera, C. (May, 2021) Flexible and Modular Brain Network Dynamics Characterize Flow Experiences During Media Use: A Mechanistic Inquiry Into Content Dynamics and Well Being.
Small Research Grant (2020) - Department of Communication at UC, Davis A Drift Diffusion Modeling Approach for Testing Mood Management Theory PI: Dr. Richard Huskey