Chenyan Jia is a postdoctoral scholar in The Program on Democracy and the Internet (PDI), jointly based at the Center on Philanthropy and Civil Society (Stanford PACS) and the Cyber Policy Center (CPC) at Stanford University. In 2023 Fall, she will be joining Northeastern University as an Assistant Professor in the School of Journalism in the College of Arts, Media, and Design with a joint appointment in the Khoury College of Computer Sciences. She has been working as a research associate for UT's Human–AI Interaction Lab.
Her research interests lie at the intersection of communication and human-computer interaction. Her work has examined (a) the influence of emerging media technologies such as automated journalism and misinformation detection algorithms on people’s political attitudes and news consumption behaviors; (b) the political bias in news coverage through NLP techniques; (c) how to leverage AI technologies to reduce bias and promote democracy.
Her research typically appears in mass communication journals and top-tier AI and HCI venues including Journal of Artificial Intelligence, Mass Communication and Society, Media and Communication, CSCW, ICWSM, EMNLP, ICLR, and AAAI. Her research has been awarded the Best Paper Award at AAAI 21. She was the recipient of the Harrington Dissertation Fellowship and Dallas Morning News Graduate Fellowship for Journalism Innovation. She received her Ph.D. from The University of Texas at Austin.
Nathaniel Persily, Postdoctoral Faculty Sponsor
Trust in COVID-19 public health information.
Journal of the Association for Information Science and Technology
Understanding the factors that influence trust in public health information is critical for designing successful public health campaigns during pandemics such as COVID-19. We present findings from a cross-sectional survey of 454 US adults-243 older (65+) and 211 younger (18-64) adults-who responded to questionnaires on human values, trust in COVID-19 information sources, attention to information quality, self-efficacy, and factual knowledge about COVID-19. Path analysis showed that trust in direct personal contacts (B = 0.071, p = .04) and attention to information quality (B = 0.251, p < .001) were positively related to self-efficacy for coping with COVID-19. The human value of self-transcendence, which emphasizes valuing others as equals and being concerned with their welfare, had significant positive indirect effects on self-efficacy in coping with COVID-19 (mediated by attention to information quality; effect = 0.049, 95% CI 0.001-0.104) and factual knowledge about COVID-19 (also mediated by attention to information quality; effect = 0.037, 95% CI 0.003-0.089). Our path model offers guidance for fine-tuning strategies for effective public health messaging and serves as a basis for further research to better understand the societal impact of COVID-19 and other public health crises.
View details for DOI 10.1002/asi.24712
View details for PubMedID 36246042
View details for PubMedCentralID PMC9538952
- Trust in COVID-19 public health information JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY 2022