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Data Science Applications and Implications in Legal Studies: A Perspective Through Topic Modelling

Tan, Jinzhe, Wan, Huan, Yan, Ping, Zhu, Zhen (2022) Data Science Applications and Implications in Legal Studies: A Perspective Through Topic Modelling. Journal of Data Science, . ISSN 1680-743X. (doi:10.6339/22-JDS1058) (KAR id:96048)

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https://doi.org/10.6339/22-JDS1058

Abstract

Law and legal studies has been an exciting new field for data science applications whereas the technological advancement also has profound implications for legal practice. For example, the legal industry has accumulated a rich body of high quality texts, images and other digitised formats, which are ready to be further processed and analysed by data scientists. On the other hand, the increasing popularity of data science has been a genuine challenge to legal practitioners, regulators and even general public and has motivated a long-lasting debate in the academia focusing on issues such as privacy protection and algorithmic discrimination. This paper collects 1236 journal articles involving both law and data science from the platform Web of Science to understand the patterns and trends of this interdisciplinary research field in terms of English journal publications. We find a clear trend of increasing publication volume over time and a strong presence of high-impact law and political science journals. We then use the Latent Dirichlet Allocation (LDA) as a topic modelling method to classify the abstracts into four topics based on the coherence measure. The four topics identified confirm that both challenges and opportunities have been investigated in this interdisciplinary field and help offer directions for future research.

Item Type: Article
DOI/Identification number: 10.6339/22-JDS1058
Uncontrolled keywords: artificial intelligence, law, literature review, text mining
Subjects: H Social Sciences > HA Statistics
K Law > K Law (General)
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Funders: [135] University of Kent
Depositing User: Zhen Zhu
Date Deposited: 04 Aug 2022 13:32 UTC
Last Modified: 06 Sep 2022 10:56 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/96048 (The current URI for this page, for reference purposes)
Zhu, Zhen: https://orcid.org/0000-0003-0258-1454
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