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Modeling Propagation of Financial Fraud: A Case Study of Cryptocurrency Scam in Social Networks
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 73 |
| Year of Publication: 2026 |
| Authors: Naglaa Mostafa, Hatem Abdelkader, Asmaa Ali |
10.5120/ijca2026926242
|
Naglaa Mostafa, Hatem Abdelkader, Asmaa Ali . Modeling Propagation of Financial Fraud: A Case Study of Cryptocurrency Scam in Social Networks. International Journal of Computer Applications. 187, 73 ( Jan 2026), 54-58. DOI=10.5120/ijca2026926242
Abstract
Information diffusion in social networks enables rapid knowledge sharing but also facilitates the viral spread of misinformation. This duality becomes particularly dangerous in the context of financial schemes. The OneCoin cryptocurrency scam exploited social media platforms and personal networks to spread deceptive narratives about its legitimacy. Utilizing emotional triggers such as the fear of missing out (FOMO), trust in influencers, and fabricated blockchain claims, the scam reached millions globally, exploiting the structure and dynamics of social networks, including echo chambers. Influencer hubs played a crucial role in the speed and scale of this misinformation cascade. This study aims to investigate how misinformation related to financial fraud propagates through social networks. It focuses on the OneCoin case to understand the mechanisms of influence, diffusion patterns, and the role of social structures in the sustainability of misinformation. By analyzing user impact, engagement behavior, and viral spread patterns, the objective is to propose data-driven strategies to detect, contain, and ultimately prevent the future dissemination of fraudulent content. We employed a multi-method analytical approach that combines quantitative and structural techniques. Data was sourced from YouTube and social media posts related to OneCoin and Ruja Ignatova. Metrics, including average views, engagement rates, and influencer activity, were analyzed over time. We integrated network analysis models to identify key propagation nodes and cascades, and applied sentiment and hashtag economic analysis to evaluate the virality of information. Findings reveal that the One Coin misinformation campaign achieved broad reach through early influencer amplification, repeated emotional appeals, and minimal counter-narratives. The average engagement rate was 0.76%, with significant spikes during orchestrated events. These results underscore the urgency of early detection systems grounded in network science.
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