Paper: arXiv 2409.00843

Abstract

Blockchain technology and decentralized finance (DeFi) are reshaping global financial systems. Despite their impact, the spatial distribution of public sentiment and its economic and geopolitical determinants are often overlooked. This study analyzes over 150 million geo-tagged, DeFi-related tweets from 2012 to 2022, sourced from a larger dataset of 7.4 billion tweets. Using sentiment scores from a BERT-based multilingual classification model, we integrated these tweets with economic and geopolitical data to create a multimodal dataset. Employing techniques like sentiment analysis, spatial econometrics, clustering, and topic modeling, we uncovered significant global variations in DeFi engagement and sentiment. Our findings indicate that economic development significantly influences DeFi engagement, particularly after 2015. Geographically weighted regression analysis revealed GDP per capita as a key predictor of DeFi tweet proportions, with its impact growing following major increases in cryptocurrency values such as bitcoin. While wealthier nations are more actively engaged in DeFi discourse, the lowest-income countries often discuss DeFi in terms of financial security and sudden wealth. Conversely, middle-income countries relate DeFi to social and religious themes, whereas high-income countries view it mainly as a speculative instrument or entertainment. This research advances interdisciplinary studies in computational social science and finance and supports open science by making our dataset and code available on GitHub, and providing a non-code workflow on the KNIME platform. These contributions enable a broad range of scholars to explore DeFi adoption and sentiment, aiding policymakers, regulators, and developers in promoting financial inclusion and responsible DeFi engagement globally.

Complexity vs Empirical Score

  • Math Complexity: 4.0/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The study uses spatial econometrics and regression, which involves some mathematical complexity, but it is not excessively dense or advanced. The empirical rigor is high due to the analysis of over 150 million geo-tagged tweets, integration with economic data, use of BERT-based sentiment analysis, and public availability of datasets and code.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Spatiotemporal Analysis of Global<br>DeFi Sentiment & Determinants"] --> B["Data Collection & Processing<br>150M geo-tagged tweets, 2012-2022<br>+ Economic/Geopolitical Data"]
  B --> C["Core Methodology<br>Sentiment Analysis (BERT)<br>Spatial Econometrics & Clustering<br>Topic Modeling"]
  C --> D["Key Finding 1<br>DeFi Engagement driven by<br>Economic Development & GDP"]
  C --> E["Key Finding 2<br>Sentiment Themes differ by Income Level<br>Speculation (High) vs Security (Low)"]

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1 of 82 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.