Central Bank Communication with Public: Bank of England and Twitter (X)

2025-06-03

AI Summary

This study investigates the drivers of public engagement with tweets posted by the Bank of England (2007–2022). Leveraging 3.13 million public mentions, it integrates large-scale time-series text mining, multidimensional content coding (media type, readability, topic), event-driven volatility analysis, and elastic regression modeling to quantify, for the first time, central bank social media engagement elasticity (mean = 1.095). Results show cultural narrative content (e.g., Turing banknotes) elicits 1,300× more engagement than policy-related posts; videos and images increase engagement by 1,700% and 126%, respectively; evening posting yields peak engagement despite lowest frequency—revealing temporal misalignment; high readability and policy announcements consistently boost all participation metrics; Brexit induces pronounced elasticity volatility. The core contribution lies in uncovering nonlinear dissemination patterns, empirically confirming that media modality effects substantially outweigh posting frequency effects, and establishing a novel, evidence-based framework for evaluating central banks’ digital communication efficacy.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data
• Natural Language Processing: Discourse, Pragmatics & Argument Mining

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Economics, Online Markets and Human Computation: The sharing economy
• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization

Abstract

Central banks increasingly use social media to communicate beyond financial markets, yet evidence on public engagement effectiveness remains limited. Despite 113 central banks joining Twitter between 2008 and 2018, we lack understanding of what drives audience interaction with their content. To examine engagement determinants, we analyzed 3.13 million tweets mentioning the Bank of England from 2007 to 2022, including 9,810 official posts. We investigate posting patterns, measure engagement elasticity, and identify content characteristics predicting higher interaction. The Bank's posting schedule misaligns with peak audience engagement times, with evening hours generating the highest interaction despite minimal posting. Cultural content, such as the Alan Turing 50 pound note, achieved 1,300 times higher engagement than routine policy communications. Engagement elasticity averaged 1.095 with substantial volatility during events like Brexit, contrasting with the Federal Reserve's stability. Media content dramatically increased engagement: videos by 1,700 percent, photos by 126 percent, while monetary policy announcements and readability significantly enhanced all metrics. Content quality and timing matter more than posting frequency for effective central bank communication. These findings suggest central banks should prioritize accessible, media-rich content during high-attention periods rather than increasing volume, with implications for digital communication strategies in fulfilling public transparency mandates.

Problem

• Determinants of public engagement with central bank Twitter content
• Misalignment between Bank of England posting schedule and peak engagement times
• Impact of content type and timing on audience interaction metrics

Innovation

• Analyzed 3.13 million tweets for engagement patterns
• Identified evening hours as peak engagement times
• Found media-rich content boosts engagement significantly