Authors: Walter Hernandez, Kamil Tylinski, Alastair Moore, Niall Roche, Nikhil Vadgama, Horst Treiblmaier, Jiangbo Shangguan, Paolo Tasca, Jiahua Xu

Published on: August 23, 2023

Impact Score: 8.3

Arxiv code: Arxiv:2308.1242

Summary

• What is new: An adaptable and scalable NLP-driven systematic literature review methodology combined with a unique Named Entity Recognition (NER) dataset tailored for DLT and ESG research.
• Why this is important: The rapidly evolving nature of Distributed Ledger Technologies (DLTs) and their impact on environmental and societal aspects, coupled with the sheer volume of publications, making manual literature analysis challenging.
• What the research proposes: A systematic literature review method that uses Natural Language Processing (NLP) to explore DLT’s intersection with Environmental, Social, and Governance (ESG) aspects, refining a large corpus of publications to key documents for analysis.
• Results: Developed an inaugural literature review and temporal graph analysis of DLT’s evolution in ESG contexts from 24,539 publications narrowed down to 505 key publications, demonstrating the methodology’s effectiveness in analyzing DLT’s impact.

Technical Details

Technological frameworks used: Directed citation network, transformer-based language model for NER

Models used: Transformer-based language model

Data used: 24,539 publications refined from 107 seed papers, resulting in a unique NER dataset of 54,808 entities

Potential Impact

Stakeholders in the DLT domain, including technology firms specializing in blockchain and ledger technologies, environmental and social governance consulting firms, and policy makers.

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