Skip to main content

The Frames of Romance Scamming

Pamela Faber (2024) — Research in Language

What do these research terms mean?
Preprint
A manuscript shared before formal peer review and publication. Check whether a later published version is available.
Dataset
A collection of data or examples for others to inspect or reuse. It can appear in Library search and topic mapping, but RSRC does not use dataset records as evidence in Research Insights.
Dissertation or thesis
Research submitted for an academic degree. This describes its format, not its reliability.
Journal article
An article published in a journal. This label alone does not establish peer review, study quality, or how well the findings apply elsewhere.
Qualitative research
Examines experiences, meanings, or processes, often through interviews or observations. It can explain how something happens without estimating how common it is.
Quantitative research
Uses numerical measurements to describe patterns or test relationships. A relationship between two measurements does not by itself show that one causes the other.
Systematic review
Uses a planned, documented method to find and assess research addressing a question. Its conclusions still depend on the included studies and what the search covered.
Meta-analysis
Statistically combines results from multiple studies. Combining studies does not remove weaknesses in their design or make unlike populations interchangeable.
Not classified
This record has no recognized label in this filter. It does not mean the publication used no method, or that no research exists.

Definitions draw on DataCite resource types; Cochrane review methods; NLM: association and causation. RSRC’s dataset and classification rules are explained in our methodology.

Citation tools


              
              

Transparency

Evidence and review status

This page contains AI-generated content. No human content review or subject-matter-expert review is recorded.

Source basis
Downloaded PDF
Source updates
No notice found at last check
AI-generated page content
Yes
Automated checks
Passed
Administrative approval
Yes
Human content review
Not recorded
Subject-matter-expert review
Not recorded
How this was prepared
Source basis

RSRC downloaded and privately stored a copy of the paper for internal analysis. The PDF is not offered to viewers from this page.

  • PDF added to RSRC:
Source updates

No incoming update notice was found in the dated Crossref response. Coverage is incomplete, particularly for corrections and expressions of concern; this is not a guarantee that the source is valid or unchanged.

  • Last source-status attempt:
AI-generated page content

AI-generated research notes displayed on this page: Synopsis, Identified gaps, Methods, Limitations, Future work. The paper itself is not described as AI-generated.

  • Document analysis recorded:
  • Page record updated:
Automated checks

The current, source-bound synopsis passed the recorded versioned publication checks.

  • Checks completed:
View passed checks (3)
  • Length, completeness, repetition, refusal, boilerplate, and active-markup screening
  • Numerical claims checked against the available source text
  • English-source lexical grounding check
Administrative approval

The record is approved for public display, but a complete historical administrator action is not recorded.

Human content review

No human review is recorded for the AI-generated content displayed on this page.

Subject-matter-expert review

RSRC has not recorded review of this content by a subject-matter or methods expert.

Review-state definitions
Found a possible error? Request a correction.

Synopsis

The author compiled a corpus of 53 Facebook-originated conversations with self-identified male romance scammers collected from 2021 to 2023 while posing as a 57-year-old widow. The 65,251-sentence corpus was analyzed in SketchEngine using Concordance, WordSketch, Keywords, and Thesaurus modules, compared against EnglishWeb2021, and interpreted through frame semantics, scripted sub-formats, and Whitty's stage model. The analysis interprets the scam as a commercial transaction disguised as an emerging romantic relationship. The conclusion argues that persuasive language can lead targets to overlook grammatical errors and accept implausible scenarios as plausible. The corpus involves one author acting as the sole target, so interactional patterns may reflect this specific persona and response strategy rather than victim experiences generally.

Identified Gaps

The paper identifies a relative lack of research on the language of love fraud, compared with earlier work on Nigerian scam emails. It also notes that linguistic analysis should be used more often in fraud cases. The study addresses this gap by examining authentic scammer-target conversations rather than police reports or victim interviews.

Methods

The author compiled a corpus of 53 Facebook-originated conversations with self-identified male romance scammers collected from 2021 to 2023 while posing as a 57-year-old widow. The 65,251-sentence corpus was analyzed in SketchEngine using Concordance, WordSketch, Keywords, and Thesaurus modules, compared against EnglishWeb2021, and interpreted through frame semantics, scripted sub-formats, and Whitty’s stage model.

Limitations

The corpus involves one author acting as the sole target, so interactional patterns may reflect this specific persona and response strategy rather than victim experiences generally. Scammer identities and locations were largely unverified: only 18 locations were checked, and 11 of those users employed VPNs. Moreover, only 31 of 53 interactions reached the crisis stage, limiting direct evidence about payment requests.

Future Work

Future work should apply linguistic analysis more frequently to fraud cases and extend analysis beyond the present corpus. Comparative research could test whether the identified scripts, lexical patterns, and commercial-transaction framing generalize across victims, platforms, scammer groups, and relationship outcomes.

See how this publication connects to RSRC's living evidence syntheses through current citations and research-topic mapping.