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Language as Evidence pp 419–438Cite as

Palgrave Macmillan

Fighting Cybercrime through Linguistic Analysis

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Abstract

This chapter explores the phenomenon of cybercrime from a linguistic perspective. In particular, the case of romance scams is investigated to gain a better understanding of the language factors that may foster the success of the scamming process. The analysis shows that romance scams display an effective sequential structure and create an illusion of credibility, intimacy and urgency. Thus, a form of unwilling complicity with the fraudster is forced upon the victim by a dexterous orchestration of linguistic devices. More specifically, strategic lexical choices contribute to increasing involvement, and the artful combination of words and expressions pertaining to specific semantic domains—for example, love, secrecy, intimacy and money—leads the victims to comply with the scammers’ requests.

Keywords

  • Applied Societal Discourse Analysis
  • Authorship attribution
  • Cybercrime
  • Errors of judgement
  • Forensic linguistics
  • Online fraud
  • Online profile
  • Perceived similarity
  • Romance scam
  • Scam detection

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  • DOI: 10.1007/978-3-030-84330-4_12
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Notes

  1. 1.

    See Chapter 1, art. 1: ʻFor the purposes of this Convention: a) “computer system” means any device or a group of interconnected or related devices, one or more of which, pursuant to a program, performs automatic processing of data; b) “computer data” means any representation of facts, information or concepts in a form suitable for processing in a computer system, including a program suitable to cause a computer system to perform a function; c) “service provider” means: i) any public or private entity that provides to users of its service the ability to communicate by means of a computer system, and ii) any other entity that processes or stores computer data on behalf of such communication service or users of such service; d) “traffic data” means any computer data relating to a communication by means of a computer system, generated by a computer system that formed a part in the chain of communication, indicating the communication’s origin, destination, route, time, date, size, duration, or type of underlying serviceʼ. Retrieved from:

    https://rm.coe.int/CoERMPublicCommonSearchServices/DisplayDCTMContent?documentId=0900001680081561. (Last access 30 March 2020).

  2. 2.

    See https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex:52007DC0267. (Last access 30 March 2020).

  3. 3.

    US Supreme Court case, Daubert v. Merrell Dow Pharmaceuticals, 509 US 579 (1993).

  4. 4.

    Among the different approaches to authorship identification we can find two main models: generative (e.g. Bayesian) or discriminative (e.g. Support Vector Machine). Also, two main classes are identified: closed or open. In the closed class, the expert attributes the text to a single author drawn from a predefined group, while, in the open class, the possible author does not necessarily belong to a predefined set. On a final note, in the case of profiling, the expert identifies the author’s general properties or characteristics—for example, socio-demographic features (Inches et al., 2013). For a general introduction to authorship identification practices, see Stamatatos (2009).

  5. 5.

    Instant messages tend to be very informal, short and unstructured. However, in scamming, the textual organisation may vary according to different variables, such as the replicability of the texts and the use of templates. In the case of conversational documents, the classical statistical models are unsuitable for authorship attribution, and ad hoc approaches need to be implemented to attain a high accuracy rate (Inches et al., 2013).

  6. 6.

    Olayinka Ilumsa Sunmola was the leader of a successful and far-reaching scamming organisation targeting female victims, especially in the US. The crimes were perpetrated between 2007 and 2014.

  7. 7.

    All texts were anonymised and potentially sensitive pieces of information were deleted.

  8. 8.

    The data obtained by Suarez-Tangil et al. (2019) are drawn from datingnmore.com and the related public scam list is available at scamdigger.com.

  9. 9.

    For the sake of authenticity, any errors or inaccuracies present in the original texts have been preserved in the excerpts quoted.

  10. 10.

    The term refers to the label adopted by the users themselves.

  11. 11.

    Qualitative research in this field is often based on a phenomenological approach. The project from which this study derives also includes interviews with the victims to offer an emic perspective on the phenomenon. Although these aspects are beyond the scope of this chapter, they are deemed essential to gain novel and authentic insights into the scamming process.

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Authors and Affiliations

  1. University of Bergamo, Bergamo, Italy

    Patrizia Anesa

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  1. Patrizia Anesa
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Correspondence to Patrizia Anesa .

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Editors and Affiliations

  1. Departamento de Filología Inglesa, University of Alicante, Alicante, Spain

    Assoc. Prof. Victoria Guillén-Nieto

  2. Anglistik III Englische Sprachwissenschaft, Heinrich Heine University Düsseldorf, Düsseldorf, Germany

    Prof. Dieter Stein

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Anesa, P. (2022). Fighting Cybercrime through Linguistic Analysis. In: Guillén-Nieto, V., Stein, D. (eds) Language as Evidence. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-84330-4_12

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  • DOI: https://doi.org/10.1007/978-3-030-84330-4_12

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