Bitcoin's Edge: Embedded Sentiment in Blockchain Transactional Data
- URL: http://arxiv.org/abs/2504.13598v1
- Date: Fri, 18 Apr 2025 10:06:21 GMT
- Title: Bitcoin's Edge: Embedded Sentiment in Blockchain Transactional Data
- Authors: Charalampos Kleitsikas, Nikolaos Korfiatis, Stefanos Leonardos, Carmine Ventre,
- Abstract summary: We use Natural Language Processing techniques to analyze, detect patterns, and extract public sentiment encoded within blockchain transactional data.<n>Our findings shed light on a previously underexplored source of freely available, transparent and immutable data.
- Score: 5.762286612061954
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Cryptocurrency blockchains, beyond their primary role as distributed payment systems, are increasingly used to store and share arbitrary content, such as text messages and files. Although often non-financial, this hidden content can impact price movements by conveying private information, shaping sentiment, and influencing public opinion. However, current analyses of such data are limited in scope and scalability, primarily relying on manual classification or hand-crafted heuristics. In this work, we address these limitations by employing Natural Language Processing techniques to analyze, detect patterns, and extract public sentiment encoded within blockchain transactional data. Using a variety of Machine Learning techniques, we showcase for the first time the predictive power of blockchain-embedded sentiment in forecasting cryptocurrency price movements on the Bitcoin and Ethereum blockchains. Our findings shed light on a previously underexplored source of freely available, transparent, and immutable data and introduce blockchain sentiment analysis as a novel and robust framework for enhancing financial predictions in cryptocurrency markets. Incidentally, we discover an asymmetry between cryptocurrencies; Bitcoin has an informational advantage over Ethereum in that the sentiment embedded into transactional data is sufficient to predict its price movement.
Related papers
- BlockFound: Customized blockchain foundation model for anomaly detection [47.04595143348698]
BlockFound is a customized foundation model for anomaly blockchain transaction detection.
We introduce a series of customized designs to model the unique data structure of blockchain transactions.
BlockFound is the only method that successfully detects anomalous transactions on Solana with high accuracy.
arXiv Detail & Related papers (2024-10-05T05:11:34Z) - IT Strategic alignment in the decentralized finance (DeFi): CBDC and digital currencies [49.1574468325115]
Decentralized finance (DeFi) is a disruptive-based financial infrastructure.
This paper seeks to answer two main questions 1) What are the common IT elements in the DeFi?
And 2) How the elements to the IT strategic alignment in DeFi?
arXiv Detail & Related papers (2024-05-17T10:19:20Z) - Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning [51.13534069758711]
Decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities.
Federated Learning (FL) enables participants to collaboratively train models while safeguarding data privacy.
This paper investigates the synergy between blockchain's security features and FL's privacy-preserving model training capabilities.
arXiv Detail & Related papers (2024-03-28T07:08:26Z) - Blockchain Metrics and Indicators in Cryptocurrency Trading [0.22940141855172028]
The objective of this paper is the construction of new indicators that can be useful to operate in the cryptocurrency market.
These indicators are based on public data obtained from the blockchain network, specifically from the nodes that make up Bitcoin mining.
arXiv Detail & Related papers (2024-02-11T12:34:58Z) - Interplay between Cryptocurrency Transactions and Online Financial
Forums [41.94295877935867]
This study focuses on the study of the interplay between these cryptocurrency forums and fluctuations in cryptocurrency values.
It shows that the activity of Bitcointalk forum keeps a direct relationship with the trend in the values of BTC.
The experiment highlights that forum data can explain specific events in the financial field.
arXiv Detail & Related papers (2023-11-27T16:25:28Z) - Predicting Digital Asset Prices using Natural Language Processing: a
survey [2.806897141084325]
The rise of Machine Learning, and Natural Language Processing, in particular, has shed light monitoring and predicting the price behaviors of cryptocurrencies.
This paper aims to review and analyze the recent efforts in applying Machine Learning and Natural Language Processing methods to predict the prices and analyze the behaviors of digital assets such as Bitcoin.
arXiv Detail & Related papers (2022-11-28T17:37:06Z) - Towards Measuring the Traceability of Cryptocurrencies [0.5371337604556311]
We put forward a formal framework to measure the (un)traceability and anonymity of cryptocurrencies.
Our work provides the first practical, efficient, and probabilistic measure to assess the traceability of cryptocurrencies.
We implement and extensively evaluate our proposed traceability measure on several cryptocurrency transaction graphs.
arXiv Detail & Related papers (2022-11-08T14:08:39Z) - Token Spammers, Rug Pulls, and SniperBots: An Analysis of the Ecosystem of Tokens in Ethereum and in the Binance Smart Chain (BNB) [50.888293380932616]
We study the ecosystem of the tokens and liquidity pools.
We find that about 60% of tokens are active for less than one day.
We estimate that 1-day rug pulls generated $240 million in profits.
arXiv Detail & Related papers (2022-06-16T14:20:19Z) - Analysis of Arbitrary Content on Blockchain-Based Systems using BigQuery [0.0]
We develop and apply a cloud-based approach for quickly discovering and classifying content on public blockchains.
Our method can be adapted to different blockchain systems and offers insights into content-related usage patterns and potential cases of abuse.
To the best of our knowledge, the presented study is the first to systematically analyze non-financial content stored on the blockchain.
arXiv Detail & Related papers (2022-03-17T15:12:38Z) - Quantum-resistance in blockchain networks [46.63333997460008]
This paper describes the work carried out by the Inter-American Development Bank, the IDB Lab, LACChain, Quantum Computing (CQC), and Tecnologico de Monterrey to identify and eliminate quantum threats in blockchain networks.
The advent of quantum computing threatens internet protocols and blockchain networks because they utilize non-quantum resistant cryptographic algorithms.
arXiv Detail & Related papers (2021-06-11T23:39:25Z) - A Blockchain Transaction Graph based Machine Learning Method for Bitcoin
Price Prediction [8.575998118995216]
Existing bitcoin prediction works mostly on trivial feature engineering.
We propose k-order transaction graph to reveal patterns under different scope.
A novel prediction method is proposed to accept the features and make price prediction, which can take advantage from particular patterns from different history period.
arXiv Detail & Related papers (2020-08-21T20:08:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.