Global Public Sentiment on Decentralized Finance: A Spatiotemporal Analysis of Geo-tagged Tweets from 150 Countries

2024-09-01

arXiv.org

AI Summary

This study investigates the spatiotemporal evolution of public sentiment toward decentralized finance (DeFi) across 150 countries from 2012 to 2022 and identifies its macroeconomic and geopolitical drivers. Method: Leveraging 150 million geotagged tweets, we integrate a multilingual BERT sentiment model (pretrained on 7.4 billion tweets), geographically weighted regression (GWR), LDA topic modeling, and World Development Indicators (WDI) to construct the first cross-national, spatiotemporal analytical framework for crypto-finance sentiment. Contribution/Results: We find that sentiment intensity is higher—but actual participation lower—in low- and middle-income countries; topic diffusion is driven more by economic similarity than geographic proximity; and Bitcoin price surges significantly strengthen the correlation between GDP and tweet activity. The study delivers an open-source dataset, reproducible code, and fully documented analysis pipeline, providing empirical foundations for evidence-based DeFi regulation and sustainable financial policy design.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Economics, Online Markets and Human Computation: Economic aspects of blockchain and cryptocurrencies
• Security and Privacy: Data transparency and provenance

Abstract

In the digital era, blockchain technology, cryptocurrencies, and non-fungible tokens (NFTs) have transformed financial and decentralized systems. However, existing research often neglects the spatiotemporal variations in public sentiment toward these technologies, limiting macro-level insights into their global impact. This study leverages Twitter data to explore public attention and sentiment across 150 countries, analyzing over 150 million geotagged tweets from 2012 to 2022. Sentiment scores were derived using a BERT-based multilingual sentiment model trained on 7.4 billion tweets. The analysis integrates global cryptocurrency regulations and economic indicators from the World Development Indicators database. Results reveal significant global sentiment variations influenced by economic factors, with more developed nations engaging more in discussions, while less developed countries show higher sentiment levels. Geographically weighted regression indicates that GDP-tweet engagement correlation intensifies following Bitcoin price surges. Topic modeling shows that countries within similar economic clusters share discussion trends, while different clusters focus on distinct topics. This study highlights global disparities in sentiment toward decentralized finance, shaped by economic and regional factors, with implications for poverty alleviation, cryptocurrency crime, and sustainable development. The dataset and code are publicly available on GitHub.

Problem

• Decentralized Finance (DeFi)
• Public Perception
• Economic and Political Factors

Innovation

• Big Data
• Social Media Analysis
• Decentralized Finance (DeFi) Insights