Departments
Journalism
Education
Ph.D., The University of Texas at Austin
Awards
Harrington Dissertation Fellowship
AAAI-21 Outstanding Paper Award: Special Track on AI for Social Impact
The Dallas Morning News Fellowship for Journalism Innovation
Chenyan Jia is an Assistant Professor in the School of Journalism and Media Innovation with a joint appointment in Khoury College of Computer Sciences at Northeastern University. Prior to joining Northeastern University, she spent one year as a postdoctoral scholar at Stanford University. She received her Ph.D. from The University of Texas at Austin.
Her research interests lie at the intersection of mass communication and human-computer interaction. Her work has examined (a) the influence of emerging media technologies on people’s attitudes and behaviors; (b) how to design human-centered AI and how to align AI with human values; (c) how to mitigate the spread of misinformation. Her research typically appears in mass communication journals and top-tier AI and HCI venues including New Media & Society, Mass Communication and Society, Journal of Artificial Intelligence, CSCW, ICWSM, and AAAI. Her research has been awarded the Best Paper Award at AAAI 21. Her research was supported by Stanford Institute for Human-Centered Artificial Intelligence (HAI), UT Human–AI Interaction Lab, and Stanford Cyber Policy Center. She was the recipient of the Harrington Dissertation Fellowship.
Research/Publications Highlights
Jia, C., Lam, M. S., Mai, M. C., Hancock, J. T. Bernstein, M. S. (2023). Embedding democratic values into social media AIs via societal objective functions. In Proceedings of the ACM: Human-Computer Interaction. Issue CSCW (CSCW 2024).
Jia, C., Riedl, J. M., Woolley, S. (2023). Promises and perils of automated journalism: algorithms, experimentation, and ‘teachers of machines’ in China and the United States. Journalism Studies.
Lee, T., Johnson, T., Jia, C., & Lacasa, I. (2023). How social media users become misinformed: The roles of news-finds-me perception and misinformation exposure in COVID-19 misperception. New Media & Society.
Jia, C., Boltz, A., Zhang, A., Chen, A., & Lee, M. K. (2022). Understanding effects of algorithmic vs. community label on perceived accuracy of hyper-partisan misinformation. In Proceedings of the ACM: Human-Computer Interaction. Issue CSCW (CSCW 2022).
Liu, R., Jia, C., Wei, J., Xu, G., & Vosoughi, S. (2022). Quantifying and alleviating political bias in language models. Artificial Intelligence (AIJ).
Jia, C., & Liu, R. (2021). Algorithmic or human source? Examining relative hostile media effect with a transformer-based framework. Media and Communication. 9(4), 170 – 181.
Jia, C., & Johnson, T. (2021). Source credibility matters: Does automated journalism inspire selective exposure? International Journal of Communication. 15(2021), 3760–3781.