mlsanityData to 7 Oct 2026

ScamSpot: Fighting Financial Fraud in Instagram Comments

arXiv 2402.08869 ↗First posted 14 Feb 2024cs.AIcited by 1

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Abstract (arXiv)

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/.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 3 of its 18 references that are in the map. First posted on arXiv 14 Feb 2024.

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3 of 18 references.

References · 3

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2023Open, Closed, or Small Language Models for Text Classification?cited by 34
  3. 2022Universal Spam Detection using Transfer Learning of BERT Modelcited by 13

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