Chang'an University | China
This scholarly profile represents an advanced researcher working at the intersection of computational communication and large language model studies, with a strong foundation in human–artificial intelligence interaction. The research agenda centers on understanding how algorithmic systems simulate, shape, Public Opinion Simulation and Algorithmic Fidelity in Social Contexts and respond to public communication across complex sociopolitical environments, particularly within non-Western and regulated information contexts. Core contributions include large-scale empirical investigations into language model–generated social data, offering critical insights into the reliability, bias, and cultural embeddedness of algorithmic outputs. A key theoretical advancement lies in redefining algorithmic fidelity as a context-dependent concept, moving beyond surface-level accuracy toward a multidimensional framework that evaluates response behavior, distributional alignment, and subgroup representation. Methodologically, the work integrates survey-based validation, robustness testing across multiple model architectures, and comparative analysis to uncover stable structural patterns as well as systemic limitations in simulated public opinion. These findings provide practical guidance for the responsible application of generative models in communication research, policy analysis, and media studies. With extensive publication experience in high-impact academic journals and active engagement in international scholarly communities, this body of work contributes to advancing ethical, culturally aware, and empirically grounded approaches to artificial intelligence–mediated communication research.