arXiv:2606.16874v1 Announce Type: new Abstract: Online scam behavior is inherently multi-stage, and the lifecycle includes temporally ordered rails and events rather than isolated signals. Existing works analyze characteristics of scam types and rails, but they do not track scam trends across years. Moreover, the work on the relations between rails is hampered due to the lack of open-source datasets with annotations and coverage of different scam types. To address these gaps, we build a dataset to analyze the yearly trend of scam characteristics and rail paths using Reddit self-disclosure narr
The proliferation of online platforms and AI capabilities makes understanding and predicting scam trends increasingly critical, necessitating more systematic data collection.
This research provides a more granular understanding of evolving scam methodologies, which is crucial for developing proactive countermeasures and protecting users in an increasingly digital world.
The ability to track scam trends across years and analyze rail paths offers a new dimension for cybersecurity and financial fraud prevention, moving beyond isolated incident analysis.
- · Cybersecurity firms
- · Financial institutions
- · Law enforcement agencies
- · Social media platforms
- · Scam perpetrators
- · Vulnerable online users (if analysis isn't applied)
Improved detection and prevention mechanisms for online scams.
Potential for AI-driven systems to predict and disrupt scam operations before they cause significant harm.
A shift in cybercriminal tactics towards more sophisticated, less traceable methods in response to enhanced detection.
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Read at arXiv cs.CL