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

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 cryptocurrencies price prediction and smart contracts, leaving domain-specific language under explored despite the sector's ~$3 trillion market capitalization and rapid technological evolution.

In summary, our contributions are:

• DLT-Corpus: 2.98 billion tokens from 22.12 million documents (37,440 scientific publications, 49,023 patents, 22M social media posts) with rich metadata enabling cross-disciplinary research.

• Innovation diffusion analysis: Evidence that Distributed Ledger Technologies (DLTs) follow traditional technology transfer patterns, with research preceding market expansion and creating a virtuous funding cycle.

• Sentiment analysis dataset: 23,301 cryptocurrency news headlines and brief descriptions with crowdsourced annotations from active community members, addressing the need for domain-specific labeled data.

• LedgerBERT: A domain-adapted language model achieving 23% improvement over BERT-base on DLT-specific NER task, developed through continued pre-training of SciBERT.

DLT-Corpus, sentiment analysis dataset, models, and code are publicly available to support reproducibility and future research.