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

Tan, Jinzhe; Wan, Huan; Yan, Ping; Zhu, Zhen

doi:10.6339/22-JDS1058

Journal of Data Science

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

Volume 21, Issue 1 (2023), pp. 57–67

Jinzhe Tan Huan Wan Ping Yan All authors (4)

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

Pub. online: 4 August 2022 Type: Data Science In Action

Open Access

Received
29 December 2021

Accepted
2 July 2022

Published
4 August 2022

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.

Supplementary material

Supplementary Material

The file “JDS_dataScienceLaw.ipynb” has the Python code used for the analysis above. The file “articles_en.csv” has the original data collected from Web of Science. The file “README.txt” has the description of the two files above.

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2023 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.

Open access article under the CC BY license.

Keywords

artificial intelligence law literature review text mining

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