The Online Mutual Help Practices of Romance Fraud Victims

Cantin, Pascale-Marie ; Ream, Fyscillia ; Dupont, Benoît (2025) — Les Presses de l’Université d’Ottawa | University of Ottawa Press eBooks

Synopsis (AI-Generated)

This publication analyzes how romance fraud victims use online mutual help communities to cope with and mitigate the harm they experience. The authors frame online mutual help as a bottom-up form of “security of self” that complements conventional institutions. The study asks how victims leverage discussion forums to tell their stories, obtain practical advice, and support others, while also documenting how these forums can function as repositories of actionable information about scammers and as platforms for raising public awareness. Methodologically, the chapter relies on a hybrid approach that combines automated collection of large forum datasets with in-depth qualitative analysis. Three forums— ScamWarners, ScamSurvivors, and Signal-Arnaques—were selected for their content richness. A sample of one thousand conversations (approximately 335 per forum) was analyzed using inductive coding to identify three core themes: (1) supporting victims by helping them speak, regain a voice, and access personalized guidance; (2) identifying fraudulent profiles through shared exchanges, digital traces, and indicators of compromise; and (3) raising awareness through tips, red flags, and guidance on reporting and prevention. The findings illustrate how victims may move from novice to experienced “surviving” status, with experienced members mentoring others and contributing to collective knowledge. The chapter concludes that these mutual help practices can reveal risk patterns, aid prevention, and supplement formal response avenues, while also signaling the need for systematic evaluation, governance considerations, and potential collaboration with authorities to balance support with privacy and safety concerns. It stops short of endorsing any universal policy, emphasizing careful assessment of benefits and limitations.

Identified Gaps (AI-Generated)

Research on romance fraud is minimal in Canada and beyond and has chiefly addressed fraud networks and victim consequences. The authors identify no prior research on post-incident experiences or victims’ use of online mutual-help strategies to mitigate harm and rebuild. Few empirical studies examine online mutual help among crime victims, its benefits for victims, or the efficacy of fraud-related mutual-help practices.

Methods (AI-Generated)

The study used a hybrid design: automated collection of public forum data and qualitative content analysis. A custom Python script extracted post metadata and content from ScamWarners, ScamSurvivors, and Signal-Arnaques. Researchers purposively selected 1,000 high-interaction conversations (335 per forum), based on original-post length, views, and replies, imported them into QDA Miner, and inductively coded them. Analysis derived three themes: supporting victims, identifying fraudulent profiles, and raising awareness.

Limitations (AI-Generated)

The conversation sample was restricted to permit in-depth qualitative analysis and selected using post length and interaction measures, which may underrepresent less visible or shorter exchanges. Platform-use statistics are historical and forum-specific: the Facebook/Skype data cover ScamWarners posts from 2012–2021, while email-provider data cover ScamSurvivors posts from 2013–2021; the authors note platform rankings may have changed since 2021. The chapter does not evaluate the efficacy of mutual-help practices.

Future Work (AI-Generated)

Systematically and rigorously assess the benefits, shortcomings, and efficacy of online mutual-help practices for fraud victims; examine whether they can be scaled to improve cyber-resilience. Extend research to social-media platforms where mutual help emerges organically. Investigate protective measures by email providers that may limit fake-account creation.

AI-Generated Content Notice

The synopsis and research notes on this page were generated with AI from available publication information and, when available, the uploaded paper text. They may contain errors, omissions, or interpretation issues. Readers should follow the DOI or source link, review the original publication, and make their own judgment about the content.

Found a possible error? Request a correction.