Global Public Sentiment on Decentralized Finance: A Spatiotemporal Analysis of Geo-tagged Tweets from 150 Countries
<|reference_start|>Global Public Sentiment on Decentralized Finance: A Spatiotemporal Analysis of Geo-tagged Tweets from 150 Countries: 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.<|reference_end|>
@article{chen2024global,
    title={Global Public Sentiment on Decentralized Finance: A Spatiotemporal
  Analysis of Geo-tagged Tweets from 150 Countries},
    author={Yuqi Chen, Yifan Li, Kyrie Zhixuan Zhou, Xiaokang Fu, Lingbo Liu,
  Shuming Bao, Daniel Sui, Luyao Zhang},
    journal={arXiv preprint arXiv:2409.00843},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.00843},
    primaryClass={econ.GN cs.CE cs.CY q-fin.CP q-fin.EC stat.ML}
}