Research interests
My research program bridges computational social sciences and management. The long-term objective is to pursue big data-driven, computationally intensive theory construction. To this end, I enact a mixed-methods approach that blends machine learning and qualitative inquiry for unpacking the entanglement of collective human behaviors and digital capabilities, augmented by large language models and generative AI. The theoretical underpinnings of my research mainly stem from psychology, sociology, and organization science. Empirically, my research puts forward frameworks unifying descriptive patterns, explanatory mechanisms, and predictive indicators in social settings. For instance, my doctoral dissertation employs a natural language processing approach to quantify cultural tastes in both physical and digital spaces. At the University of À¶Ý®ÊÓƵ, my postdoctoral research will investigate trying circumstances in life and coping strategies from large-scale, cross-cultural/lingual survey data.Â
Representative publications:
- Gruzd, A., Li, Y., & Mai, P. (2025). Shaping Western Perceptions: The Role of English-language Verified Telegram Channels in Framing the Narratives Around the Russia-Ukraine War. The Journal of Communication Technology. (Forthcoming)
- Li, Y., Zadehnoori, I., Jowhar, A., Wise, S., Laplume, A., & Zihayat, M. (2024). Learning from Yesterday: Predicting early-stage startup success for accelerators through content and cohort dynamics. Journal of Business Venturing Insights, 22, e00490.
- Li, Y., Vatrapu, R., & Zihayat, M. (2023). A Systematic Review of Computational Methods in and Research Taxonomy of Homophily in Information Systems. Proceedings of the 2023 European Conference on Information Systems, Kristiansand, Norway.Â
- Malik, A., Li, Y., Karbasian, H., Hamari, J., & Johri, A. (2019). Live, Love, Juul: User and Content Analysis of Twitter Posts about Juul. American Journal of Health Behavior, 43(2), 326-336.Â