Machine Learning-Driven Sentiment Analysis of UK CBDC Tweets Versus Thematic Evaluation of Public and Institutional Perspectives: A Comprehensive Study on the Digital Pound

Abstract

In an era of rapid digital transformation and declining cash usage, central banks worldwide are exploring digital currencies to modernise monetary systems and maintain monetary sovereignty. The proposed digital pound in the UK has ignited intense debate among policymakers and citizens, particularly concerning issues of privacy, surveillance, and economic inclusion. Against this backdrop, this study investigates how public sentiment evolves in response to policy milestones and how it aligns — or diverges — from official Bank of England (BoE) narratives concerning a potential digital pound, addressing a critical knowledge gap in understanding public reception of central bank digital currencies (CBDCs). To meet this objective, the study adopts a novel interdisciplinary approach integrating advanced sentiment analysis using fine-tuned transformer models (DistilBERT, RoBERTa, XLM-RoBERTa), communication theories (e.g., framing theory, agenda-setting theory, and Grunig’s two-way symmetrical model), and analysis of policy messaging. A bespoke, domain-specific gold-standard dataset was created and validated, enabling the fine-tuning of these models. RoBERTa, trained for three epochs, emerged as the optimal model for classifying nuanced discussions related to the digital pound. Longitudinal analysis of public discourse on X (formerly Twitter) across three key periods (2020, 2023, and 2024), corresponding to major BoE policy announcements, revealed an “Exploration–Polarisation– Adaptation” sequence: initial cautious optimism evolved into pronounced negativity, particularly concerning privacy and government control, following major policy announcements, with a partial rebound after official BoE responses. A comparative thematic analysis with official BoE narratives highlighted key discrepancies, notably a “privacy framing gap” where the BoE's technically focused approach to data protection diverged from public concerns over surveillance and government overreach. This mismatch underscores a disconnect between the technocratic framing of policy narratives and public anxieties, pointing to the imperative for two-way symmetrical communication to establish trust. By illustrating how official narratives can both shape and overlook public views, this study contributes practical insights for policymakers and researchers navigating the complex interplay of technology, policy communication, and public opinion surrounding the digital pound. Recommendations include targeted public engagement on privacy, transparent implementation roadmaps, and a shift towards two-way symmetrical dialogue. While acknowledging limitations related to data source representativeness and the potential influence of external factors, this study provides a comprehensive, empirically grounded understanding of public sentiment dynamics in digital monetary policy. Future research should extend these findings by incorporating broader data modalities and demographic insights, and by applying automated hyperparameter optimisation techniques to further refine understanding of public sentiment dynamics around the digital pound and similar CBDC innovations.