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Drawing on social approval as a linguistic strategy: A discourse semantic analysis of judgement evaluation in suspected online romance scammer dating profiles

Lee, Kam-Fong ; Chan, Mei Yuit ; Ali, Afida Mohamad (2024) — Psychology of Language and Communication

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DOI: 10.58734/plc-2024-0008 (opens in a new tab)

Type:   Journal Article
Country:   Malaysia
Tags:   AI-Enabled Fraud, Appraisal Theory, Dating Profiles, Deception Detection, Linguistic Analysis, Prevention, Romance Scams, Social Approval, Victim Experience
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Synopsis

            
This mixed discourse-semantic study compared 60 suspected scammer profiles from ScamDigger with 60 general OkCupid profiles. Two trained coders applied appraisal theory's judgement categories - normality, capacity, tenacity, veracity, and propriety - through word-list searches, contextual reading, and manual Atlas.ti coding, then compared normalized frequencies. Tenacity language, including loyalty and commitment, was the clearest distinction: it appeared 80 times in suspected scammer profiles and 22 times in the comparison profiles. The result suggests that conspicuous commitment language may help construct a persuasive dating persona, but it is not a validated detection rule. Nonrandom samples, unverified comparison profiles, omitted demographic controls, and different profile-writing interfaces limit attribution and generalization.

          

        
                  

            
Identified Gaps

            
The study identifies limited understanding of language used after initial profile creation: subsequent scammer–victim interactions require discourse- and conversation-level analysis. It also indicates a need for more representative, balanced comparison data across dating platforms and demographic/contextual categories, because the current general-profile corpus may include unknown fraudulent accounts.

          

        
                  

            
Methods

            
Mixed qualitative–quantitative discourse-semantic corpus study. Researchers compared 60 suspected scammer dating profiles from scamdigger.com (30 declared male, 30 declared female) with 60 general OkCupid profiles. Using appraisal theory’s judgement framework, two trained human coders identified normality, capacity, tenacity, veracity, and propriety expressions through word-list searches, contextual close reading, and manual coding in Atlas.ti. They compared normalized category frequencies using log-likelihood ratios and effect sizes.

          

        
                  

            
Limitations

            
The specially built corpora used non-random sampling and were not fully representative. Sampling balanced only declared gender; it excluded age, occupation, and geography. General comparison profiles could not be verified as genuine and may contain fraudulent profiles. Different platform designs may have biased results: OkCupid offers profile-writing prompts, whereas datingnmore.com permits free writing. Restricting profiles to narratives exceeding 80 words partially mitigated, but did not remove, this platform difference.

          

        
                  

            
Future Work

            
Examine subsequent scammer–victim interactions at discourse and conversation level to identify manipulation strategies. Build more representative general-user comparison corpora using random sampling, multiple dating portals, and demographic/contextual clusters. Translate linguistic descriptions of scam strategies into public-awareness efforts and linguistic detection tools for suspicious communications.

          

              

    
    

        
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