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Fighting Cybercrime through Linguistic Analysis

Patrizia Anesa (2022) — Language as Evidence

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DOI: 10.1007/978-3-030-84330-4_12 (opens in a new tab)

Country:   Global
Tags:   Authorship Attribution, Corpus Linguistics, Cybercrime, Forensic Linguistics, Lexical Analysis, Online Deception, Persuasion, Romance Scams
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Synopsis

            
Patrizia Anesa’s chapter, published in Language as Evidence in 2022, investigates how linguistic analysis can aid in understanding and combatting cybercrime, with a focus on online romance scams. The author argues for an interdisciplinary, international approach that combines forensic linguistics with applied societal discourse analysis to describe, explain, and suggest potential interventions for romance fraud. The study relies on an authentic corpus of romance scam texts (ORSC) collected from the United States and Malaysia between 2016 and 2018, and it pairs qualitative coding with quantitative corpus methods to illuminate how deceptive texts are constructed and received. Methodologically, the chapter blends forensic-linguistic techniques with sociolinguistic analysis to identify persuasive strategies and profiles used by scammers. Anesa describes the role of voice analysis, authorship attribution, and stylistic features, along with lexical patterns across semantic fields such as religion, love, money, and secrecy. The research employs a mixed-methods workflow: qualitative coding using QDA Miner Lite with intercoder reliability checks, followed by quantitative analysis with AntConc to assess word frequency and keyness. The author also notes limitations related to small corpus size and the complexity of attributing texts to single authors within a collaborative scam operation. Key findings indicate that romance scammers construct online personas linked to specific professional and geographic scripts (often military or engineering figures abroad) and deploy emotionally evocative language and long self-descriptions to foster trust. Semantics and discourse cues—especially those tied to love, financial requests, and secrecy—frequently appear and may influence victims’ judgments. The chapter discusses implications for improving early detection, awareness, and crime-prevention strategies, while acknowledging limitations and the need for complementary methods and broader data.

          

        
                  

            
Identified Gaps

            
Naturalistic research on online deception is less common than laboratory research. The chapter identifies a need to understand the determinants of victims' compliance despite fraud indicators. It also notes that cybercrime concepts and investigative methods require continual revision because the phenomenon changes rapidly. Victim-perspective evidence is needed to assess the proposed explanation of judgment errors.

          

        
                  

            
Methods

            
The chapter analyzes a purpose-built corpus of authentic online romance-scam texts, including texts linked to an international criminal organization and messages and emails collected in Malaysia from 2016 to 2018. It combines forensic linguistics and Applied Societal Discourse Analysis. Two coders qualitatively coded lexical semantic fields in QDA Miner Lite, achieving 85% agreement; a third coder resolved discrepancies. AntConc was then used to examine word frequency and keyness.

          

        
                  

            
Limitations

            
The corpus is relatively small, and the findings are described as only a fragment of a complex reality. Access to primary romance-scam data is difficult because of sensitivity and ethical concerns. The corpus chiefly contains male-scammers' texts targeting women and involves people mainly located in the United States and Malaysia, limiting generalizability. Claimed-profile profession counts are indicative rather than precise because categories overlap.

          

        
                  

            
Future Work

            
Conduct victim interviews to obtain an emic perspective and test the study's hypothesis about victims' errors of judgment. Complement corpus analysis with in situ observation approaches such as scambaiting, while accounting for how baiter behavior affects data authenticity and validity. Develop awareness efforts around recurrent linguistic strategies to support earlier detection.

          

              

    
    

        
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