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Evidence Synthesis: Technology and AI in Romance, Catfishing, and Pig‑Butchering Scams

Published Updated Romance Scam Research Center
Publications
51
Evidence statements
187
Cited sources
51
Synthesis version
67

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Across the reviewed, human‑curated evidence, social media and general-purpose platforms (not only dating apps) are repeatedly identified as common initiation channels for romance and relationship-based fraud; offenders frequently move conversations from those public-facing platforms into less‑monitored messaging services to continue grooming and evade detection. Generative AI and deepfake tools are accessible and have been documented or plausibly attributed in some cases to support fabricated identities (images, voices, or videos), reducing the reliability of simple provenance checks such as reverse-image search. Empirical and qualitative studies document semi-industrialized offender practices (assembled digital infrastructure, role allocation, scripted materials) and partial automation (email-harvesting, semi-automated scripts, fake-profile farms), but the precise share of operations run end-to-end by large language models (LLMs) or fully automated systems is unknown. Romance-initiated investment fraud (“pig-butchering”) commonly uses fabricated trading dashboards, links/QR distribution, crypto transfers, mixers, and on‑chain recirculation patterns that complicate full forensic attribution. Prototype detection and OSINT workflows (logistic classifiers, Maltego, reverse-image guidance) report promising in‑sample results but are limited by dataset selection, validation gaps, privacy trade‑offs for dynamic message analysis, and uneven external evaluation. Methodologically, the corpus is heterogeneous and often relies on purposive, self‑selected, or secondary-sourced datasets; authors consistently identify these constraints and call for interdisciplinary, privacy‑sensitive real‑world evaluations, living forensic syntheses, and targeted empirical measurement of AI’s operational role in scams. [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16]

Platform ecology and channel shifting

Multiple empirical sources identify social media and general-purpose platforms (not only purpose-built dating apps) as frequent initiation channels for romance and relationship-based fraud. Large complaint and survey datasets and qualitative reports repeatedly list Facebook, Instagram and similar social-network sites among the most commonly reported first-contact origins. [1] [17] [18]

Authors document a consistent pattern of platform mixing: initial contact often begins on a visible public platform and then shifts to less‑monitored messaging applications (for example WhatsApp, Telegram, Google Chat) where grooming and payment requests continue. Some studies report that operations commonly employ two to three distinct applications during a single scam. [19] [20] [2]

Platform affordances and public profile content are described as enabling targeted, tailored narratives: publicly visible friend lists, hobby posts, and profile details provide material offenders use to craft plausible ‘love‑story’ pretexts. Researchers therefore highlight social‑media profile structure and algorithmic friend-suggestion features as factors that can facilitate target selection. [21] [22] [23]

AI, deepfakes, and synthetic media

Multiple sources document that accessible deepfake and generative‑AI tools can produce realistic synthetic images, voices, and videos and that these tools require lower technical thresholds than earlier synthetic‑media methods. Authors explicitly link these capabilities to potential increases in deceptive plausibility for romance fraud and catfishing. [3] [5]

Researchers and commentators emphasize practical consequences for verification: reverse-image and provenance checks sometimes fail (for example, searches returning no matches), and AI‑generated unique images reduce linkability to a real source, undermining existing consumer-facing verification strategies. [8] [4] [24] [14]

The reviewed evidence does not support a validated estimate of how many operations are fully automated by LLMs or end‑to‑end synthetic systems. Several authors note this proportion is unknown and call for empirical measurement of (a) the operational role of generative AI in live scams and (b) the effectiveness of deepfake detection tools in real-world settings. [25] [15] [26] [27]

Automation, industrialization, and offender infrastructure

Qualitative and courtroom-sourced studies document assembled digital infrastructure used by offenders: false social‑media and dating profiles, dedicated email accounts, foreign phone numbers, SIM changes, VPNs, and centralized IT resources that support multi-step operations and laundering. This evidence supports the characterization of some operations as semi‑industrialized rather than ad hoc individual efforts. [28] [29] [30] [31]

Empirical work also documents partial automation in acquisition and early stages: email‑harvesting, automated initial-contact processes, and honeypot experiments indicate low but measurable automated engagement rates; yet platform‑specific studies show significant manual interaction remains necessary for sustained romance scams, which limits claims of full automation. [32] [33] [34] [35]

Authors caution that technical countermeasures (improved verification, fake‑profile detection) raise an adaptive risk: offenders change impersonated identities and tactics when scripts become well known, so detection improvements can shift offender behavior or migration across platforms rather than fully eliminating threats. [36] [37] [38] [39]

Pig‑butchering, cryptocurrency interfaces, and forensic tracing

Multiple mixed‑method and forensic studies characterize ‘pig‑butchering’ as a hybrid romance‑to‑investment fraud where long‑term trust-building funnels victims into fabricated trading dashboards or apps. Empirical case series and reviews document distribution of fake apps/websites via links or QR codes and use of simulated dashboards to sustain investment-style manipulation. [7] [6] [40] [10]

Single‑case and small‑series forensic investigations combine OSINT, Maltego link analysis, and on‑chain/off‑chain linkage to produce partial attribution (wallet → social account → platform), but authors repeatedly report limitations such as missing email headers, unverifiable wallet–identity links, and incomplete subpoenaed data that prevent conclusive attribution in many cases. [8] [11] [41]

Because of these attribution limits, the literature recommends living forensic syntheses and real‑time collaboration between researchers and forensic analysts to keep pace with laundering techniques and to validate methods on continuously updated seeds and ground truth when available rather than relying on static, retrospective datasets alone. [10] [12]

Detection prototypes, OSINT workflows, and privacy trade‑offs

Other technical detection approaches include automated reverse‑image searches, stylometric and LIWC analyses, multimodal systems combining behavioral, IP, photographic and text signals, and proposed voice‑biometric tools to identify impersonation. Authors emphasize contextual and language limits of lexical tools and recommend combining modalities to reduce false positives. [27] [42] [43] [44]

Prototypes in the batch combine open-source OSINT data collection (including Maltego transforms on romance-scam websites), TF‑IDF feature extraction on cleaned emails and links, and a logistic-regression classifier that assigns a suspiciousness score to each data point. One reported evaluation, using a 75/25 train/test split on a dataset compiled from online resources and Maltego-derived information, produced 85% accuracy, 88% precision, 80% recall, and an F1 score of 0.84. Future work noted by the authors includes expanding the dataset and exploring more complex machine-learning algorithms. [12]

Several authors explicitly identify privacy and governance trade‑offs: dynamic analysis of private messages could improve early warning or trigger advisories but would intrude on user privacy and raise acceptability questions. Work in this area calls for research on user acceptance, transparency, bias, and governance before operational deployment of message‑monitoring interventions. [13]

Authors recommend partnership research with platforms to access higher-quality labeled data and to test detection systems under realistic, threat‑adaptive conditions rather than relying solely on in‑sample performance claims from curated OSINT datasets and laboratory splits alone. Future work proposals include integrating more complex ML, expanding scenario coverage, and conducting platform-partnered evaluations. [12] [45] [46]

Methodological patterns, limitations, evidence gaps, and future directions

The reviewed corpus is methodologically heterogeneous: qualitative interviews, complaint and administrative reports, corpus linguistics, blockchain forensics, case files, offender interviews, and prototype evaluations are all present. Many studies rely on purposive, self‑selected, or secondary-sourced datasets (public complaints, forums, news reports), which authors consistently identify as limiting generalizability and prevalence estimation. [1] [4] [15] [3] [12]

Commonly reported methodological limitations include uncertain offender attribution (VPNs, caller‑ID masking), incomplete forensic seed data, unverifiable victim‑reported addresses, small purposive samples, and dataset contamination (controls containing unlabelled scam profiles). Researchers explicitly link these constraints to cautious interpretation of prevalence or causality claims. [2] [8] [11] [46] [47] [48]

Evidence gaps identified within the literature (and explicitly recommended by authors) include: (a) empirical measurement of how generative AI and LLMs are operationally used in live scams, (b) real‑world validation of automated detection systems with platform data, (c) living forensic syntheses that integrate on‑chain and off‑chain evidence, and (d) robust evaluation of privacy‑sensitive early‑warning or advisory systems. [25] [27] [10] [12] [13] [49]

Across the corpus, authors repeatedly call for interdisciplinary, platform‑partnered research that balances technical detection, legal and policy remedies, and ethical acceptability, and they caution that any deployed interventions must be evaluated for false‑positive risk, bias, privacy intrusion, and likely offender adaptation. [50] [13] [51] [31]

References

  1. Grace Carvalho Fernandes, S.; BASTOS DO NASCIMENTO, V.; Castelo Branco Do Nascimento, V. (2023). Romance Scam: Victim Manipulation and Human Trafficking DOI
  2. Faber, P. (2024). The Frames of Romance Scamming DOI
  3. Cross, C. (2022). Using artificial intelligence (AI) and deepfakes to deceive victims: the need to rethink current romance fraud prevention messaging DOI
  4. Cross, C. (2023). “I knew it was a scam”: Understanding the triggers for recognizing romance fraud DOI
  5. Gauci, Christine; Vella, Mary Grace (2026). Love and Technology DOI
  6. Cross, C. (2023). Romance baiting, cryptorom and ‘pig butchering’: an evolutionary step in romance fraud DOI
  7. Wang, F.; Zhou, X. (2022). Persuasive Schemes for Financial Exploitation in Online Romance Scam: An Anatomy on Sha Zhu Pan (杀猪盘) in China DOI
  8. Botha, Johannes George; Singh, Kreaan; Leenen, Louise (2025). Analysis of a Cryptocurrency Investment Scam: Pig Butchering DOI
  9. Ditasya Anisa Riani; Ruslan Abdul Gani; Maryani, Maryani (2026). Catfishing on Social Media: A Criminal Law And Islamic Criminal Law Analysis In Jambi DOI
  10. Gujarathi, Palak; Verma, Shankey; Nair, Vipin Vijay (2026). Pig Butchering Scams as Cyber-Enabled Financial Crime: A Scoping Review of Dimensions, Modus Operandi, and Victim-Offender Dynamics DOI
  11. Griffin, John M.; Mei, Kevin (2024). How Do Crypto Flows Finance Slavery? The Economics of Pig Butchering DOI
  12. Vedhanayagam, Priya; Singh, Moulik; Dadhania, Arora Preksha; Ikram, Sumaiya Thaseen (2026). Forensic footprints in digital love: Unveiling romance scams with Maltego and machine learning DOI
  13. Dickerson, S.; Apeh, E.; Ollis, G. (2020). Contextualised Cyber Security Awareness Approach for Online Romance Fraud DOI
  14. Kassem, R.; Carter, E. (2023). Mapping romance fraud research – a systematic review DOI
  15. Cross, C.; Holt, TJ. (2023). More than Money: Examining the Potential Exposure of Romance Fraud Victims to Identity Crime DOI
  16. Shaari, AH.; Kamaluddin, MR.; Paizi@Fauzi, WF.; Mohd, M. (2019). Online-Dating Romance Scam in Malaysia: An Analysis of Online Conversations between Scammers and Victims DOI
  17. Dr. Rhem Rick N. Corpuz; Mary Joy G. Galang; Kim Hope S. Gueco; Kate D. Pamintuan (2025). ROMANCE AND RUIN: THE INTERPLAY OF FINANCIAL LOSS AND PSYCHOLOGICAL WELL-BEING AMONG LOVE SCAM VICTIMS IN THE PHILIPPINES DOI
  18. Alavi, Khadijah; Mahbob, Maizatul Haizan; Sooed, Mohammad Syahrul Azha (2020). Strategi Komunikasi Penjenayah Cinta Siber Terhadap Wanita Profesional DOI
  19. Dickinson, T.; Wang, F.; Maimon, D. (2023). What Money Can Do: Examining the Effects of Rewards on Online Romance Fraudsters’ Deceptive Strategies DOI
  20. Ordekian, Marilyne; Papasavva, Antonis; Mariconti, Enrico; Vasek, Marie (2024). A Sinister Fattening: Dissecting the Tales of Pig Butchering and Other Cryptocurrency Scams DOI
  21. Christian Kopp; Robert Layton; Jim Sillitoe; Iqbal Gondal (2016). The Role of Love stories in Romance Scams: A Qualitative Analysis of Fraudulent Profiles DOI
  22. Unknown (2023). The Crime of "Pig-butchering Scams" in the Securities Market and Legal Regulation DOI
  23. Lamphere, RD.; Lucas, KT. (2019). Online Romance in the 21st Century DOI
  24. Cross, C.; Layt, R. (2021). “I Suspect That the Pictures Are Stolen”: Romance Fraud, Identity Crime, and Responding to Suspicions of Inauthentic Identities DOI
  25. Dominguez Castillo, Lorena (2026). Industrialized heartbreak: how generative AI enables romance fraud at scale DOI
  26. Herrera, LD.; Hastings, J. (2024). The Trajectory of Romance Scams in the U.S DOI
  27. Shapiro, LR. (2022). Online Romance Scammers DOI
  28. Abubakari, Yushawu; Lazarus, Suleman; Oseh-Ovarah, Valeen (2026). A crime script perspective on mapping the entry, continuation, and exit pathways of online romance fraud DOI
  29. Yetunde O. Ogunleye; Ojedokun, Usman A.; Adeyinka A. Aderinto (2020). Pathways and Motivations for Cyber Fraud Involvement among Female Undergraduates of Selected Universities in South-West Nigeria DOI
  30. Luong, Hai Thanh; Ngo, Hieu Minh (2024). Understanding the Nature of the Transnational Scam-Related Fraud: Challenges and Solutions from Vietnam’s Perspective DOI
  31. Hasibuan, Juneidi; Syam, Syafrudin (2023). A Legal Analysis on Online Fraud Using Fake Identity DOI
  32. Atta-Asamoah, Andrews (2009). Understanding the West African cyber crime process DOI
  33. Jakobsson, Markus (2016). Understanding Social Engineering Based Scams DOI
  34. Huang, JingMin; Stringhini, Gianluca; Yong, Peng (2015). Quit Playing Games with My Heart: Understanding Online Dating Scams DOI
  35. Robinson, Jemima; Edwards, Matthew (2024). Fraudsters target the elderly: Behavioural evidence from randomised controlled scam-baiting experiments DOI
  36. Kim, Hyo-shin; Seo, Jun-bae (2019). A Study on Romance Scam : The Current Situation and Effective Countermeasures DOI
  37. Abubakari, Yushawu; Oseh-Ovarah, Valeen (2025). The Gamification of Online Romance Fraud through Offenders’ Cards DOI
  38. Diallo, Ousmane; Hassan, Hasnani; Najeeb Zaidan, Muslim (2025). Leveraging Naive Bayesian Machine Learning for Detecting Pig Butchering Scams: A Cybersecurity Social Engineering Perspective in Africa DOI
  39. Valentin Le Normand (2026). Romance Scam Statistics 2024-2026: verified, source-attributed figures from FBI IC3, FTC, City of London Police, Cybermalveillance.gouv.fr, GASA and Chainalysis (Baromètre 2026 des arnaques sentimentales) DOI
  40. Franceschini, Ivan; Li, Ling; Bo, Mark (2023). Compound Capitalism: A Political Economy of Southeast Asia’s Online Scam Operations DOI
  41. Reiter, Jonathan; Team, Bitrace (2024). Connecting Chinese and American Scam Victims DOI
  42. Ma, Katelyn Wan Fei; McKinnon, Tammy (2021). COVID-19 and cyber fraud: emerging threats during the pandemic DOI
  43. Hamsi, Ahmad Safwan; Bahry, Farrah Diana Saiful; Tobi, Siti Noraini Mohd; Masrom, Maslin (2015). Cybercrime over Internet Love Scams in Malaysia: A Discussion on the Theoretical Perspectives, Connecting Factors and Keys to the Problem DOI
  44. Toma, Catalina L.; Hancock, Jeffrey T. (2012). What Lies Beneath: The Linguistic Traces of Deception in Online Dating Profiles DOI
  45. Rabby, F.; Chowdory, MMU. (2024). Romance Scamming: Uncovering the Transnational Crime and Legal Challenges DOI
  46. Lee, KF.; Chan, MY.; Mohamad Ali, A. (2022). Self and desired partner descriptions in the online romance scam: a linguistic analysis of scammer and general user profiles on online dating portals DOI
  47. Lee, KF; Chan, MY; Ali, Afida Mohamad (2024). Drawing on social approval as a linguistic strategy: A discourse semantic analysis of judgement evaluation in suspected online romance scammer dating profiles DOI
  48. Khukhunaishvili, Dina (2024). Romance Scam as one of the main Challenges of cybercrime DOI
  49. Sorell, T.; Whitty, M. (2019). Online romance scams and victimhood DOI
  50. Boland, Michael James (2025). Developments in the Law Governing Online Activity: The Criminalisation of Catfishing and Civil Relief in Cases of Image-Based Sexual Abuse DOI
  51. Krause, David (2025). The Deceptive Allure: Understanding and Combating Cryptocurrency Pig Butchering Scams DOI

This AI-assisted synthesis is based on reviewed evidence records. It may contain errors or omissions. Follow the publication and DOI links, consult the original works, and make your own judgment about the evidence.

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This synthesis version cites 51 public RSRC records. The broader Library currently contains 226 approved publications mapped to this topic. Newly mapped publications can appear in the topic collection before they are incorporated into a later synthesis version.

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View 51 cited publication records from synthesis version 67
  1. Citation 1
    Romance Scam: Victim Manipulation and Human Trafficking

    Grace Carvalho Fernandes, S.; BASTOS DO NASCIMENTO, V.; Castelo Branco Do Nascimento, V. (2023) Anais do Congresso Internacional de Relações Internacionais do Amazonas: a Amazônia no mundo e o mundo na Amazônia

  2. Citation 2
    The Frames of Romance Scamming

    Faber, P. (2024) Research in Language

  3. Citation 3
    Using artificial intelligence (AI) and deepfakes to deceive victims: the need to rethink current romance fraud prevention messaging

    Cross, C. (2022) Crime Prevention and Community Safety

  4. Citation 4
    “I knew it was a scam”: Understanding the triggers for recognizing romance fraud

    Cross, C. (2023) Criminology & Public Policy

  5. Citation 5
    Love and Technology

    Gauci, Christine; Vella, Mary Grace (2026) The Palgrave Handbook of Global Social Problems

  6. Citation 6
    Romance baiting, cryptorom and ‘pig butchering’: an evolutionary step in romance fraud

    Cross, C. (2023) Current Issues in Criminal Justice

  7. Citation 7
    Persuasive Schemes for Financial Exploitation in Online Romance Scam: An Anatomy on Sha Zhu Pan (杀猪盘) in China

    Wang, F.; Zhou, X. (2022) Victims & Offenders

  8. Citation 8
    Analysis of a Cryptocurrency Investment Scam: Pig Butchering

    Botha, Johannes George; Singh, Kreaan; Leenen, Louise (2025) European Conference on Cyber Warfare and Security

  9. Citation 9
    Catfishing on Social Media: A Criminal Law And Islamic Criminal Law Analysis In Jambi

    Ditasya Anisa Riani; Ruslan Abdul Gani; Maryani, Maryani (2026) International Journal of Islamic Education, Research and Multiculturalism (IJIERM)

  10. Citation 10
    Pig Butchering Scams as Cyber-Enabled Financial Crime: A Scoping Review of Dimensions, Modus Operandi, and Victim-Offender Dynamics

    Gujarathi, Palak; Verma, Shankey; Nair, Vipin Vijay (2026) Deviant Behavior

  11. Citation 11
    How Do Crypto Flows Finance Slavery? The Economics of Pig Butchering

    Griffin, John M.; Mei, Kevin (2024) SSRN Electronic Journal

  12. Citation 12
    Forensic footprints in digital love: Unveiling romance scams with Maltego and machine learning

    Vedhanayagam, Priya; Singh, Moulik; Dadhania, Arora Preksha; Ikram, Sumaiya Thaseen (2026) AIP conference proceedings

  13. Citation 13
    Contextualised Cyber Security Awareness Approach for Online Romance Fraud

    Dickerson, S.; Apeh, E.; Ollis, G. (2020) 2020 7th International Conference on Behavioural and Social Computing (BESC)

  14. Citation 14
    Mapping romance fraud research – a systematic review

    Kassem, R.; Carter, E. (2023) Journal of Financial Crime

  15. Citation 15
    More than Money: Examining the Potential Exposure of Romance Fraud Victims to Identity Crime

    Cross, C.; Holt, TJ. (2023) Global Crime

  16. Citation 16
    Online-Dating Romance Scam in Malaysia: An Analysis of Online Conversations between Scammers and Victims

    Shaari, AH.; Kamaluddin, MR.; Paizi@Fauzi, WF.; Mohd, M. (2019) GEMA Online® Journal of Language Studies

  17. Citation 17
    ROMANCE AND RUIN: THE INTERPLAY OF FINANCIAL LOSS AND PSYCHOLOGICAL WELL-BEING AMONG LOVE SCAM VICTIMS IN THE PHILIPPINES

    Dr. Rhem Rick N. Corpuz; Mary Joy G. Galang; Kim Hope S. Gueco; Kate D. Pamintuan (2025) EPRA International Journal of Multidisciplinary Research (IJMR)

  18. Citation 18
    Strategi Komunikasi Penjenayah Cinta Siber Terhadap Wanita Profesional

    Alavi, Khadijah; Mahbob, Maizatul Haizan; Sooed, Mohammad Syahrul Azha (2020) Jurnal Komunikasi: Malaysian Journal of Communication

  19. Citation 19
    What Money Can Do: Examining the Effects of Rewards on Online Romance Fraudsters’ Deceptive Strategies

    Dickinson, T.; Wang, F.; Maimon, D. (2023) Deviant Behavior

  20. Citation 20
    A Sinister Fattening: Dissecting the Tales of Pig Butchering and Other Cryptocurrency Scams

    Ordekian, Marilyne; Papasavva, Antonis; Mariconti, Enrico; Vasek, Marie (2024) 2024 APWG Symposium on Electronic Crime Research (eCrime)

  21. Citation 21
    The Role of Love stories in Romance Scams: A Qualitative Analysis of Fraudulent Profiles

    Christian Kopp; Robert Layton; Jim Sillitoe; Iqbal Gondal (2016) Zenodo (CERN European Organization for Nuclear Research)

  22. Citation 22
    The Crime of "Pig-butchering Scams" in the Securities Market and Legal Regulation

    Unknown (2023) Science of Law Journal

  23. Citation 23
    Online Romance in the 21st Century

    Lamphere, RD.; Lucas, KT. (2019) Advances in Media, Entertainment, and the Arts

  24. Citation 24
    “I Suspect That the Pictures Are Stolen”: Romance Fraud, Identity Crime, and Responding to Suspicions of Inauthentic Identities

    Cross, C.; Layt, R. (2021) Social Science Computer Review

  25. Citation 25
    Industrialized heartbreak: how generative AI enables romance fraud at scale

    Dominguez Castillo, Lorena (2026) AI and Ethics

  26. Citation 26
    The Trajectory of Romance Scams in the U.S

    Herrera, LD.; Hastings, J. (2024) 2024 12th International Symposium on Digital Forensics and Security (ISDFS)

  27. Citation 27
    Online Romance Scammers

    Shapiro, LR. (2022) Cyberpredators and Their Prey

  28. Citation 28
    A crime script perspective on mapping the entry, continuation, and exit pathways of online romance fraud

    Abubakari, Yushawu; Lazarus, Suleman; Oseh-Ovarah, Valeen (2026) International Journal of Comparative and Applied Criminal Justice

  29. Citation 29
    Pathways and Motivations for Cyber Fraud Involvement among Female Undergraduates of Selected Universities in South-West Nigeria

    Yetunde O. Ogunleye; Ojedokun, Usman A.; Adeyinka A. Aderinto (2020) Zenodo

  30. Citation 30
    Understanding the Nature of the Transnational Scam-Related Fraud: Challenges and Solutions from Vietnam’s Perspective

    Luong, Hai Thanh; Ngo, Hieu Minh (2024) Laws

  31. Citation 31
    A Legal Analysis on Online Fraud Using Fake Identity

    Hasibuan, Juneidi; Syam, Syafrudin (2023) Indonesian Journal of Multidisciplinary Science

  32. Citation 32
    Understanding the West African cyber crime process

    Atta-Asamoah, Andrews (2009) African Security Review

  33. Citation 33
    Understanding Social Engineering Based Scams

    Jakobsson, Markus (2016)

  34. Citation 34
    Quit Playing Games with My Heart: Understanding Online Dating Scams

    Huang, JingMin; Stringhini, Gianluca; Yong, Peng (2015) Lecture Notes in Computer Science

  35. Citation 35
    Fraudsters target the elderly: Behavioural evidence from randomised controlled scam-baiting experiments

    Robinson, Jemima; Edwards, Matthew (2024) Security Journal

  36. Citation 36
    A Study on Romance Scam : The Current Situation and Effective Countermeasures

    Kim, Hyo-shin; Seo, Jun-bae (2019) The Police Science Journal

  37. Citation 37
    The Gamification of Online Romance Fraud through Offenders’ Cards

    Abubakari, Yushawu; Oseh-Ovarah, Valeen (2025) Digital Threats: Research and Practice

  38. Citation 38
    Leveraging Naive Bayesian Machine Learning for Detecting Pig Butchering Scams: A Cybersecurity Social Engineering Perspective in Africa

    Diallo, Ousmane; Hassan, Hasnani; Najeeb Zaidan, Muslim (2025) The 5th International Scientific Conference on Administrative and Financial Sciences (CIC-ISCAFS'2025)

  39. Citation 39
    Romance Scam Statistics 2024-2026: verified, source-attributed figures from FBI IC3, FTC, City of London Police, Cybermalveillance.gouv.fr, GASA and Chainalysis (Baromètre 2026 des arnaques sentimentales)

    Valentin Le Normand (2026)

  40. Citation 40
    Compound Capitalism: A Political Economy of Southeast Asia’s Online Scam Operations

    Franceschini, Ivan; Li, Ling; Bo, Mark (2023) Critical Asian Studies

  41. Citation 41
    Connecting Chinese and American Scam Victims

    Reiter, Jonathan; Team, Bitrace (2024) SSRN Electronic Journal

  42. Citation 42
    COVID-19 and cyber fraud: emerging threats during the pandemic

    Ma, Katelyn Wan Fei; McKinnon, Tammy (2021) Journal of Financial Crime

  43. Citation 43
    Cybercrime over Internet Love Scams in Malaysia: A Discussion on the Theoretical Perspectives, Connecting Factors and Keys to the Problem

    Hamsi, Ahmad Safwan; Bahry, Farrah Diana Saiful; Tobi, Siti Noraini Mohd; Masrom, Maslin (2015) Journal of Management Research

  44. Citation 44
    What Lies Beneath: The Linguistic Traces of Deception in Online Dating Profiles

    Toma, Catalina L.; Hancock, Jeffrey T. (2012) Journal of Communication

  45. Citation 45
    Romance Scamming: Uncovering the Transnational Crime and Legal Challenges

    Rabby, F.; Chowdory, MMU. (2024) International Journal of Law and Societal Studies

  46. Citation 46
    Self and desired partner descriptions in the online romance scam: a linguistic analysis of scammer and general user profiles on online dating portals

    Lee, KF.; Chan, MY.; Mohamad Ali, A. (2022) Crime Prevention and Community Safety

  47. Citation 47
    Drawing on social approval as a linguistic strategy: A discourse semantic analysis of judgement evaluation in suspected online romance scammer dating profiles

    Lee, KF; Chan, MY; Ali, Afida Mohamad (2024) Psychology of Language and Communication

  48. Citation 48
    Romance Scam as one of the main Challenges of cybercrime

    Khukhunaishvili, Dina (2024)

  49. Citation 49
    Online romance scams and victimhood

    Sorell, T.; Whitty, M. (2019) Security Journal

  50. Citation 50
    Developments in the Law Governing Online Activity: The Criminalisation of Catfishing and Civil Relief in Cases of Image-Based Sexual Abuse

    Boland, Michael James (2025) SSRN Electronic Journal

  51. Citation 51
    The Deceptive Allure: Understanding and Combating Cryptocurrency Pig Butchering Scams

    Krause, David (2025) SSRN Electronic Journal

Revision history

This history includes approved public synthesis versions. Drafts and unsuccessful generation attempts are not shown.

  1. Version 67

    Current public version

    Automated claim-level support repair round 1, correcting 1 passage from synthesis version 66.

  2. Version 66

    Automated evidence-grounded revision attempt 1. Initial version.

    • Publications: 51 (-9)
    • Evidence statements: 187 (-21)
    • Cited sources: 51 (-9)

    Sources removed: A new type of victim? Profiling survivors of modern slavery in the online scam industry in Southeast Asia; Beyond Dollars: Unveiling the Deeper Layers of Online Romance Scams Introducing “Body Scam”; CHAPTER 10 The Online Mutual Help Practices of Romance Fraud Victims; Modus Operandi and Blockchain Analysis of Romance Scams: Cryptocurrency-Driven Victimization; and 5 more.

  3. Version 65

    Initial version: this synthesis is the first version produced for the controlled topic "Technology and AI" and is based only on the supplied reviewed evidence. No prior‑version evidence changes were applied.

    • Publications: 60 (+30)
    • Evidence statements: 208 (+98)
    • Cited sources: 60 (+30)

    Sources added: A Legal Analysis on Online Fraud Using Fake Identity; A new type of victim? Profiling survivors of modern slavery in the online scam industry in Southeast Asia; Beyond Dollars: Unveiling the Deeper Layers of Online Romance Scams Introducing “Body Scam”; Catfishing on Social Media: A Criminal Law And Islamic Criminal Law Analysis In Jambi; and 33 more.

    Sources removed: Anatomy of the online dating romance scam; Catching a Catfish; Facebook as a Tool of Catfishing: An Analytical Study of University Students; From swipe to swindle: a narrative review of research on older adult victims of romance fraud; and 3 more.

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