Fake advertisements detection using automated multimodal learning: a case study for Vietnamese real estate data

Nguyen, Duy; Nguyen, Trung T.; Nguyen, Cuong V.

Abstract

The popularity of e-commerce has given rise to fake advertisements that can expose users to financial and data risks while damaging the reputation of these e-commerce platforms. For these reasons, detecting and removing such fake advertisements are important for the success of e-commerce websites. In this paper, we propose FADAML, a novel end-to-end machine learning system to detect and filter out fake online advertisements. Our system combines techniques in multimodal machine learning and automated machine learning to achieve a high detection rate. As a case study, we apply FADAML to detect fake advertisements on popular Vietnamese real estate websites. Our experiments show that we can achieve 91.5% detection accuracy, which significantly outperforms three different state-of-the-art fake news detection systems.

Citation

Nguyen, D., Nguyen, T. T., & Nguyen, C. V. (2025). Fake advertisements detection using automated multimodal learning: a case study for Vietnamese real estate data. Applied Intelligence, 55(6), Article 367. https://doi.org/10.1007/s10489-025-06238-2

• Journal Article Type: Article
• Acceptance Date: Dec 30, 2024
• Online Publication Date: Jan 23, 2025
• Publication Date: 2025-04
• Deposit Date: Jan 23, 2025
• Publicly Available Date: Jan 24, 2025
• Journal: Applied Intelligence
• Print ISSN: 0924-669X
• Electronic ISSN: 1573-7497
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 55
• Issue: 6
• Article Number: 367
• DOI: https://doi.org/10.1007/s10489-025-06238-2
• Keywords: Multimodal machine learning, Fake advertisement detection, Automated machine learning
• Public URL: https://durham-repository.worktribe.com/output/3348607