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12 Crime Sci. 1 (2023)

handle is hein.journals/crimsci12 and id is 1 raw text is: Lwin Tun and Birks Crime Science (2023) 12:1 https://doi.org/10.1186/s40163-022-00177-w Crime Science Supporting crime script analyses of scams P with natural language processing Zeya Lwin Tun1 and Daniel Birks2 Abstract In recent years, internet connectivity and the ubiquitous use of digital devices have afforded a landscape of expand- ing opportunity for the proliferation of scams involving attempts to deceive individuals into giving away money or personal information. The impacts of these schemes on victims have shown to encompass social, psychological, emotional and economic harms. Consequently, there is a strong rationale to enhance our understanding of scams in order to devise ways in which they can be disrupted. One way to do so is through crime scripting, an analytical approach which seeks to characterise processes underpinning crime events. In this paper, we explore how Natu- ral Language Processing (NLP) methods might be applied to support crime script analyses, in particular to extract insights into crime event sequences from large quantities of unstructured textual data in a scalable and efficient man- ner. To illustrate this, we apply NLP methods to a public dataset of victims'stories of scams perpetrated in Singapore. We first explore approaches to automatically isolate scams with similar modus operandi using two distinct similar- ity measures. Subsequently, we use Term Frequency-Inverse Document Frequency (TF-IDF) to extract key terms in scam stories, which are then used to identify a temporal ordering of actions in ways that seek to characterise how a particular scam operates. Finally, by means of a case study, we demonstrate how the proposed methods are capable of leveraging the collective wisdom of multiple similar reports to identify a consensus in terms of likely crime event sequences, illustrating how NLP may in the future enable crime preventers to better harness unstructured free text data to better understand crime problems. Keywords Scams, Crime, Policing, Crime script analysis, Unstructured data, Natural language processing, Term frequency-inverse document frequency, Doc2Vec Introduction Scams have become increasingly prevalent alongside greater Internet connectivity and ubiquitous use of digital devices. Any person around the world can be a potential victim. Scams are as much a cause of concern in Singa- pore as they are globally. Efforts by authorities to combat scams include the establishment of the Anti-Scam Cen- tre in 2019, regular police operations against domestic *Correspondence: Zeya Lwin Tun zeya.zlt@gmailcom School of Mathematics, University of Leeds, Leeds, UK School ofLaw, Universityof Leeds, Leeds, UK BMC and transnational syndicates as well as public education campaigns. Despite such efforts, victims continued to fall prey to scams, evident from the increasing number of reported scam cases over the last few years, from 9,502 cases in 2019, to 15,756 cases in 2020 and to 46,196 cases in 2021 (Singapore Police Force, 2020a, 2020b; Lin, 2022). Scams impact victims in numerous ways. The most immediate consequence is financial loss, which in turn causes tremendous emotional stress, particularly if sig- nificant amounts of savings were involved. Victims also experience embarrassment, shame and humiliation, especially in the case of love scams (Buchanan & Whitty, 2014). Scams also have longer-term psychological effects on victims, such as increased anxiety and low self- esteem. Additionally, scams that involve loss of personal ©The Author(s) 2023.Open AccessThis article is lcensed undera Creative Commons Attribution 4.0 nternational 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 thrd party material in this artide are incuded In the artide's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not induded in the artile's Creative Commons licence and your intended use Is not permitted by statutory regulation orexceeds the permitted use,you will need to obtain permission directly from the copyright holder.To view a copy ofthis licence, visit httpi/creativecommons.org/licenses/by/4.0/.The Creative Commons Public Domain Dedication waiver (http://creativeco mmons.org/publicdomain/zero/1.O/ applies to the data made available in this artide, unless otherwise stated In a credit line to the data. RESEARCH Open Access

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