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The personal_relationship frame in love fraud

Pamela Faber (2025) — Applied Corpus Linguistics

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DOI: 10.1016/j.acorp.2025.100119 (opens in a new tab)

Type:   Journal Article
Country:   Global
Tags:   Automated Detection, Corpus Linguistics, Frame Semantics, Love Fraud, Online Deception, Relationship Scripts, Romance Scamming, Romance Scams
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Synopsis

            
This publication examines how love fraud operators construct a romantic relationship with victims by exploiting the PERSONAL_RELATION frame within FrameSemantics, focusing on how scripts, phrases, and collocations shape a believable bond. The study treats love fraud as a communicative event that unfolds through staged relationship milestones, from friendship and soulmate claims to engagement and online marriage, with the aim of extracting patterns that help explain why victims are drawn in and financially exploited. The author builds a corpus of 83 conversations between male fraudsters and a female victim, collected between January 2021 and June 2024, and analyzes it with Sketch Engine to identify high-frequency terms and co-occurrence patterns tied to relationship concepts. Methodologically, the work identifies relationships and their dimensions by mapping lexical items to FRAME elements, such as RELATIONSHIP, PERSON, MONEY, and various relationship subtypes (friendship, soulmate, engagement, marriage). The results indicate that fraudsters repeatedly deploy terms associated with destiny, fate, trust, and virtuous personal attributes to legitimate the relationship, progressing through clearly defined stages that culminate in requests for money. The analysis highlights how affective language, endearments, and idealized portrayals of the victim and the fraudster themselves are used to sustain the illusion of a genuine partnership and to press for financial aid when crises arise. The paper notes limitations, including that the data derive from a specific corpus of online romances and may reflect scripting biases among practitioners, and that the study does not claim causal generalization beyond its lexical-structural findings. Nonetheless, it suggests that frame-based linguistic analysis can contribute to automated detection and public awareness by revealing recurrent scripts and lexical patterns used to manufacture and sustain love fraud.

          

        
                  

            
Identified Gaps

            
The paper identifies a need for linguistic research beyond prior critical discourse analysis, particularly to support automated detection and public awareness. Its findings are based on a single author’s interactions, so broader evidence from authentic victim-scammer conversations is needed to assess generalizability.

          

        
                  

            
Methods

            
The author compiled a 1,043,330-word corpus of 85 documents from conversations with 83 alleged fraudsters between 2021 and 2024. The corpus was analyzed in SketchEngine using frequency, WordSketch, and Concordance modules. High-frequency words and collocates of “relationship” were manually categorized using semantic analysis and interpreted through Frame Semantics and FrameNet’s PERSONAL_RELATIONSHIP and INTENTIONAL_DECEPTION frames.

          

        
                  

            
Limitations

            
Data collection was not originally planned as a formal linguistic study. The conversations were contrived because both the author and fraudsters engaged in mutual deception, which may have biased chats. The author was the sole simulated victim, had no prior elicitation experience, and no fraudster demographic information or verified identities could be obtained. Twenty-nine conversations did not reach the crisis stage.

          

        
                  

            
Future Work

            
Test the identified lexical and semantic patterns in automated, content-based systems for detecting love fraud and deceptive discourse. Further linguistic research from other perspectives could support automated detection and public awareness.

          

              

    
    

        
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