Calvin is a DPhil candidate in Social Data Science at the Oxford Internet Institute. His research examines how digital technologies shape the diffusion of misinformation, the formation of ideology, and the dynamics of public opinion. His doctoral thesis focuses on online conspiratorial content across languages and platforms, investigating why individuals believe and share conspiracy theories, how such narratives evolve and spread over time, and which moderation strategies are most effective. Through this work, he aims to promote an informed public, strengthen trust in science and institutions, and reinforce truth-seeking discourse.
Methodologically, Calvin employs data-driven approaches, including large-scale social media analytics and natural language processing, to address theory-driven questions at the intersection of computational social science. His current research interests centre on AI meta-science (evaluating and validating AI and large language models as methods in social science research, particularly in interpretative text analysis), AI literacy (leveraging AI for misinformation debunking), and narrative intelligence (measuring narratives and framing across platforms).
Calvin holds an MPhil in Communication from the Chinese University of Hong Kong, an MA in Journalism from the University of Hong Kong, and a dual BA in Business English and International Politics from the University of International Business and Economics. His research is generously supported by the OII–Dieter Schwarz Foundation, the Alan Turing Institute–DSO Labs Singapore Collaboration Project, the China Oxford Scholarship Fund, and the Great Britain–China Educational Trust. He also serves as a research lead at the Oxford Computational Political Science Group (OCPSG).
Multilingual misinformation, social media, large language models, moral foundation theory.
This programme supports research on AI, Government and Policy.
This project seeks to understand the narrative, diffusion and socio-political effects of online conspiracy theories, concentrating on those which emerged during the COVID-19 pandemic.
