The tinder swindler: Analyzing public sentiments of romance fraud using machine learning and artificial intelligence

Lokanan, ME. (2023) — Journal of Economic Criminology

DOI: 10.1016/j.jeconc.2023.100023

Synopsis

This article surveys public responses to romance fraud in online dating contexts, with a case-frame inspired by the phenomenon popularly summarized as "The Tinder Swindler." Positioned within the field of economic criminology, the work examines how audiences discuss, understand, and respond to alleged manipulative schemes conducted through dating platforms. The objective is to map public sentiment and the perceived economic harms associated with romance fraud, and to identify recurring narratives around trust, vulnerability, and accountability. The study employs machine learning and artificial intelligence to analyze large volumes of textual material and to extract directional attitudes, thematic content, and temporal trends. The approach aims to translate qualitative discourse into quantifiable indicators that can inform risk assessment, platform governance, and consumer protection discourse. Data are drawn from diverse public sources and are treated in aggregated form to avoid disclosure of individuals. Techniques common to sentiment analysis, topic modeling, and pattern recognition are referenced in general terms, with emphasis on validating interpretations against economic criminology concepts such as fraud incentives, loss distribution, and deterrence effects. The synthesis highlights how public sentiment may reflect perceived frequency, severity, and remedies associated with romance fraud, as well as trust in online platforms. The article discusses implications for policy, platform design, and future research directions, noting trade-offs between vigilance and privacy, and calling for continued interdisciplinary collaboration to monitor evolving threats in digital dating ecosystems.

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