# DLT-Corpus: A Large-Scale Text Collection for the Distributed Ledger Technology Domain

arXiv: https://arxiv.org/abs/2602.22045 · HTML: https://mlsanity.com/paper/2602.22045/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 88 references that are in the map.
- First posted on arXiv 25 Feb 2026.

## Builds on (most cited first, 23 of 23)

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- [Decrypting Distributed Ledger Design -- Taxonomy, Classification and Blockchain Community Evaluation](https://mlsanity.com/paper/1811.03419/) (2018)
- [textscPerseus: Tracing the Masterminds Behind Cryptocurrency Pump-and-Dump Schemes](https://mlsanity.com/paper/2503.01686/) (2025)
- [Evolution of ESG-focused DLT Research: An NLP Analysis of the Literature](https://mlsanity.com/paper/2308.12420/) (2023)
- [No Questions Asked: Effects of Transparency on Stablecoin Liquidity During the Collapse of Silicon Valley Bank](https://mlsanity.com/paper/2407.11716/) (2024)
- [Resisting Manipulative Bots in Meme Coin Copy Trading: A Multi-Agent Approach with Chain-of-Thought Reasoning](https://mlsanity.com/paper/2601.08641/) (2026)

## Abstract (arXiv)

We introduce DLT-Corpus, the largest domain-specific text collection for Distributed Ledger Technology (DLT) research to date: 2.98 billion tokens from 22.12 million documents spanning scientific literature (37,440 publications), United States Patent and Trademark Office (USPTO) patents (49,023 filings), and social media (22 million posts). Existing Natural Language Processing (NLP) resources for DLT focus narrowly on cryptocurrency price prediction and smart contracts, leaving domain-specific language underexplored despite the sector's ~3 trillion market capitalization and rapid technological evolution. We demonstrate DLT-Corpus' utility by analyzing patterns of technology emergence and market-innovation correlations. Findings reveal that technologies first appear in our scientific literature subset before reaching patents and social media, following traditional technology transfer patterns. While social media sentiment remains overwhelmingly bullish even during crypto winters, scientific and patent activity grows less tied to short-term sentiment, tracking overall market expansion in a virtuous cycle in which research precedes and enables economic growth that, in turn, funds further innovation. We release the DLT-Corpus and companion artifacts: LedgerBERT (+23% over BERT-base on DLT-specific Named Entity Recognition (NER) task), a sentiment analysis dataset of 23,301 crypto news headlines and descriptions, tools, and code.