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Department of Media and Communication Center for Communication Research

Prof. Yue (Nancy) DAI 代悅

BA (City University of Hong Kong); MA (The University of Hong Kong); PhD (Michigan State University)

Associate Professor

Staff Photo

Contact Information

Office: M5092
Phone: +(852) 3442 5966
Fax: +(852) 3442 0228
Email: nancy.dai@cityu.edu.hk
Website:
Personal CV: Personal CV
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Academic Profile

CityU Scholar
Google Scholar

Research Interests

  • Computer-mediated communication
  • Intergroup dynamics online
  • Mediated intergroup contact
  • Social influence and persuasion
  • Technology and misinformation correction
  • chatbot and social support
My research examines impression formation and social influence as they unfold through communication technologies, with a particular focus on how communication technology shapes how individuals process information, form impressions of both human and AI communicators, negotiate group-based identities, and develop understandings of public issues in digital environments. Rather than centering on any single platform or device, my work seeks to generate theoretical insights that not only apply across evolving technologies but also illuminate enduring patterns of human communication. My current research areas include the following:

(1) AI for companionship and social support
(2) Intergroup dynamics in information processing and prejudice reduction
(3) Technology and misinformation correction


Courses

  • COM5101 Communication Fundamentals
  • COM2303 Video Production and Editing
  • GE3202 Citizen Journalism and Civil Society
  • COM5104 Research Methods for Communication and New Media
  • COM4307 Video News Production and Anchoring

Selected Peer-reviewed Journal Articles

For a full publication list, please visit my Google Scholar profile.

  • Jia, W., & Dai, Y. (2026). When self-expression via information sharing affects health behavior intentions: The role of anticipated feedback and the audience’s relevance to the issue. Health Communication. Advanced online publication. https://doi.org/10.1080/10410236.2026.2662342
  • Fu, L., Dai, Y., & Jia, W. (2026). Examining ingroup persuasion and expectancy violation as competing mechanisms for biased information processing in social media. Journal of Broadcasting & Electronic Media, 70(2), 199–223. https://doi.org/10.1080/08838151.2025.2605123
  • Dai, Y., Fu, L., & Jia, W. (2025). Other-Interest and compassion as mechanisms of the warranting principle: Advancing warranting theory in the context of sponsored posts. Telematics and Informatics, 101, 102292. https://doi.org/10.1016/j.tele.2025.102292
  • Rheu, M. (MJ), Dai, Y., Meng, J., & Peng, W. (2024). When a chatbot disappoints you: Expectancy violation in human-chatbot interaction in a social support context. Communication Research. Advanced online publication. https://doi.org/10.1177/00936502231221669
  • Dai, Y., Lee, J., & Kim, J. W. (2023). AI vs. human voices: How delivery source and narrative format influence the effectiveness of persuasion messages. International Journal of Human–Computer Interaction. Advanced online publication. https://doi.org/10.1080/10447318.2023.2288734
  • Shi, J. & Dai, Y. (2023). Audience–campaign planner interaction in social media communication campaigns: How it influences intended campaign responses in the observing audience. Human Communication Research. Advanced online publication. https://doi.org/10.1093/hcr/hqad003
  • Dai, Y., Huang, Y.-H. C., Jia, W., & Cai, Q. (2022). The paradoxical effects of institutional trust on risk perception and risk management in the Covid-19 pandemic: Evidence from three societies. Journal of Risk Research. Advanced online publication. https://doi.org/10.1080/13669877.2022.2108122
  • Dai, Y., Jia, W., Fu, L., Sun, M., & Jiang, L. C. (2022). The effects of self-generated and other-generated eWOM in inoculating against misinformation. Telematics and Informatics , 101835. https://doi.org/10.1016/j.tele.2022.101835
  • Dai. Y. & Shi, J. (2022). Vicarious interactions in online support communities: The roles of visual anonymity and social identification. Journal of Computer-Mediated Communication. 27 (3), zmac006. https://doi.org/10.1093/jcmc/zmac006
  • Dai, Y. , Kim, J. W., & Jia, W. (2022). Health pandemic in the era of (mis)information: Examining the utility of using victim narrative and social endorsement of user-generated content to reduce panic buying in the U.S. Journal of Applied Communication Research. Advanced online publication. https://doi.org/10.1080/00909882.2022.2043557
  • Shi, J., & Dai, Y. (2022). Promoting favorable attitudes toward seeking counseling among people with depressive symptomatology: A masspersonal communication approach. Health Communication, 37(2), 242–254. https://doi.org/10.1080/10410236.2020.1834209
  • Meng, J., & Dai, Y. (2021). Emotional support from AI chatbots: Should a supportive partner self-disclose or not? Journal of Computer-Mediated Communication, zmab005. https://doi.org/10.1093/jcmc/zmab005
  • Shin, S. Y., Dai, Y. , Beyea, D., Prchal, B., Makki, T. W., Schlafhauser, K., & Van Der Heide, B. (2020). Curbing negativity: Influence of providing justifications about control over user-generated comments on social media. Communication Research, 47(6), 838–859. https://doi.org/10.1177/0093650218794853
  • Dai, Y., & Walther, J. B. (2018). Vicariously experiencing parasocial intimacy with public figures through observations of interactions on social media. Human Communication Research, 44, 322–342. https://doi.org/10.1093/hcr/hqy003
  • Dai, Y., Viken, G., Joo, E., & Bente, G. (2018). Risk assessment in ecommerce: How seller’s photos, reputation scores, and the stake of a transaction influence buyers’ purchase behavior and information processing. Computers in Human Behavior, 84, 342–351. https://doi-org.proxy2.cl.msu.edu/10.101