Skip to main content

Fighting Cybercrime through Linguistic Analysis

  • 283 Accesses


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.


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

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-84330-4_12
  • Chapter length: 20 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   129.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-84330-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book
USD   169.99
Price excludes VAT (USA)


  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: (Last access 30 March 2020).

  2. 2.

    See (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 and the related public scam list is available at

  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.


  • Anesa, P. (2020). Lovextortion: Persuasion strategies in romance cybercrime. Discourse, Context & Media, 35, 1–8.

    CrossRef  Google Scholar 

  • Anthony, L. (2019). AntConc. Waseda University.

    Google Scholar 

  • Barn, R., & Barn, B. (2016). An ontological representation of a taxonomy for cybercrime. Research Papers, 45. Retrieved from

  • Blythe, J. M., & Coventry, L. (2018). Costly but effective: Comparing the factors that influence employee anti-malware behaviours. Computers in Human Behavior, 87, 87–97.

    Google Scholar 

  • Bolton, A. (2014). Virtual criminology. In J. M. Miller (Ed.), The Encyclopedia of theoretical criminology (pp. 924–927). Wiley Blackwell.

    Google Scholar 

  • Buchanan, T., & Whitty, M. T. (2014). The online dating romance scam: causes and consequences of victimhood. Psychology, Crime & Law, 20, 261–283.

    CrossRef  Google Scholar 

  • Danielewicz-Betz, A. (2012). The role of forensic linguistics in crime investigation. In A. Littlejohn, & S. R. Mehta (Eds.), Language Studies: Stretching the Boundaries (pp. 93–108). Cambridge Scholars Publishing.

    Google Scholar 

  • Donalds, C., & Osei-Bryson, K. M. (2019). Toward a cybercrime classification ontology: A knowledge-based approach. Computers in Human Behavior, 92, 403–418.

    CrossRef  Google Scholar 

  • Furnell, S. (2002). Cybercrime: Vandalising the information society. Addison Wesley.

    Google Scholar 

  • Gill, M. (2013). Authentication and Nigerian letters. In S. Herring, D. Stein, & T. Virtanen (Eds.), Pragmatics of computer-mediated communication (pp. 411–436). De Gruyter.

    CrossRef  Google Scholar 

  • Hancock, J. (2007). Digital deception: When, where and how people lie online. In K. McKenna, T. Postmes, U. Reips, & A. N. Joinson (Eds.), Oxford handbook of internet psychology (pp. 287–301). Oxford University Press.

    Google Scholar 

  • Hancock, J., & Gonzales, A. (2013). Deception in computer-mediated communication. In S. Herring, D. Stein, & T. Virtanen (Eds.), Pragmatics of computer-mediated communication (pp. 363–383). De Gruyter.

    CrossRef  Google Scholar 

  • Herring, S., Stein, D., & Virtanen, T. (2013). Introduction to the pragmatics of computer-mediated communication. In S. Herring, D. Stein, & T. Virtanen (Eds.), Pragmatics of computer-mediated communication (pp. 3–32). De Gruyter.

    CrossRef  Google Scholar 

  • Inches, G., Harvey, M., & Crestani, F. (2013). Finding participants in a chat: authorship attribution for conversational documents. International conference on social computing, Alexandria, VA, 2013, 272–279.

    CrossRef  Google Scholar 

  • Jones, H., Towse, J., & Race, N. (2015). Susceptibility to email fraud: A review of psychological perspectives, data-collection methods, and ethical considerations. International Journal of Cyber Behavior: Psychology and Learning, 5(3), 13–29.

    Google Scholar 

  • Lindgren, S. (2018). A ghost in the machine: Tracing the role of ‘the digital’ in discursive processes of cybervictimization. Discourse & Communication, 12(5), 517–534.

    CrossRef  Google Scholar 

  • Modic, D., & Lea, S. (2013). Scam compliance and the psychology of persuasion. Social science research network. doi:

  • Perkins, R. C. (2018). The application of forensic linguistics in cybercrime investigations. Policing: A Journal of Policy and Practice. doi:

  • Stamatatos, E. (2009). A survey of modern authorship attribution methods. Journal of the American Society for Information Science and Technology, 60(3), 538–556.

    CrossRef  Google Scholar 

  • Suarez-Tangil, G., Edwards, M., Peersman, C., Stringhini, G., Rashid, A., & Whitty, M. (2019). Automatically dismantling online fating Fraud. Retrieved from arXiv:1905.12593.

    Google Scholar 

  • Suleman, I. (2016). Social and contextual taxonomy of cybercrime: Socio-economic theory of Nigerian cybercriminals. International Journal of Law, Crime and Justice, 47, 44–57.

    CrossRef  Google Scholar 

  • Toma, C. L. (2017). Developing online deception literacy while looking for love. Media, Culture & Society, 39(3), 423–428.

    CrossRef  Google Scholar 

  • Wall, D. S. (2005). The Internet as a conduit for criminal activity. In A. Pattavina (Ed.), Information technology and the criminal justice system (pp. 77–98). Sage Publications.

    CrossRef  Google Scholar 

  • Whitty, M. T. (2013). The scammers’ persuasive techniques model: Development of a stage model to explain the online dating romance scam. British Journal of Criminology, 53(4), 665–684.

    CrossRef  Google Scholar 

  • Whitty, M. T. (2018). Do you love me? Psychological characteristics of romance scam victims. Cyberpsychology, Behavior, and Social Networking, 21(2), 105–109.

    CrossRef  Google Scholar 

  • Whitty, M. T., & Buchanan, T. (2012). The online dating romance scam: A serious crime. Cyberpsychology, Behavior, and Social Networking, 15(3), 181–183.

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Patrizia Anesa .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Verify currency and authenticity via CrossMark

Cite this chapter

Anesa, P. (2022). Fighting Cybercrime through Linguistic Analysis. In: Guillén-Nieto, V., Stein, D. (eds) Language as Evidence. Palgrave Macmillan, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Palgrave Macmillan, Cham

  • Print ISBN: 978-3-030-84329-8

  • Online ISBN: 978-3-030-84330-4

  • eBook Packages: Social SciencesSocial Sciences (R0)