ScamSpot: Fighting Financial Fraud in Instagram Comments
This addresses the issue of financial fraud on Instagram for users and platforms, but it is incremental as it applies existing methods to a specific domain.
The authors tackled the problem of spam and fraudulent messages in Instagram comments in the financial sector by developing ScamSpot, a system that includes a browser extension, fine-tuned BERT model, and REST API, achieving public accessibility and evaluation through user feedback and model comparisons.
The long-standing problem of spam and fraudulent messages in the comment sections of Instagram pages in the financial sector claims new victims every day. Instagram's current spam filter proves inadequate, and existing research approaches are primarily confined to theoretical concepts. Practical implementations with evaluated results are missing. To solve this problem, we propose ScamSpot, a comprehensive system that includes a browser extension, a fine-tuned BERT model and a REST API. This approach ensures public accessibility of our results for Instagram users using the Chrome browser. Furthermore, we conduct a data annotation study, shedding light on the reasons and causes of the problem and evaluate the system through user feedback and comparison with existing models. ScamSpot is an open-source project and is publicly available at https://scamspot.github.io/.