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This paper presents a LINE-based chatbot that uses a large language model to classify where a conversation sits in a seven-stage romance scam model and then explains the stage and offers prevention advice. The abstract reports a working prototype but gives no accuracy figures, dataset details, or comparison to baselines, so its detection performance is unverified. Readers interested in LLM-based fraud detection or deployable consumer safety tools should look for the evaluation results in the full paper.
Real-time Chatbot Romance Scam Detection in Stage Classification
arXiv:2610.20631v1 Announce Type: cross Abstract: Romance scam has become a serious problem via social media, and the improvement of artificial intelligence enable to fake identify easily. In the study, a chatbot has developed to detect romance scam in real-time through Line Messaging API. Using the seven stage theories in the social science field, the stages of romance scam can be detected through large language model. In addition, once a romance scam is detected, the chatbot provides explanations of the stage and prevention advices through large language model. The contribution of the work provides a practical application, and can be used in real scenario for reducing the risk of romance scam.
Abstract:Romance scam has become a serious problem via social media, and the improvement of artificial intelligence enable to fake identify easily. In the study, a chatbot has developed to detect romance scam in real-time through Line Messaging API. Using the seven stage theories in the social science field, the stages of romance scam can be detected through large language model. In addition, once a romance scam is detected, the chatbot provides explanations of the stage and prevention advices through large language model. The contribution of the work provides a practical application, and can be used in real scenario for reducing the risk of romance scam.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Multimedia (cs.MM)
arXiv:2610.20631 [cs.CL]
(or arXiv:2610.20631v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.20631 Focus to learn more arXiv-issued DOI via DataCite
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