Supporting crime script analyses of scams with natural language processing

Lwin Tun, Z and Birks, D orcid.org/0000-0003-3055-7398 (2023) Supporting crime script analyses of scams with natural language processing. Crime Science, 12. 1. ISSN 2193-7680

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Item Type: Article
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© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Keywords: Scams; Crime; Policing; Crime script analysis; Unstructured data; Natural language processing; Term frequency-inverse document frequency; Doc2Vec
Dates:
  • Published: 2 February 2023
  • Published (online): 2 February 2023
  • Accepted: 12 November 2022
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Education, Social Sciences and Law (Leeds) > School of Law (Leeds)
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Grant number
Alan Turing Institute
No ref given
Depositing User: Symplectic Publications
Date Deposited: 03 Mar 2023 10:07
Last Modified: 03 Mar 2023 10:07
Status: Published
Publisher: SpringerOpen
Identification Number: 10.1186/s40163-022-00177-w
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