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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2501.10848 |
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| _version_ | 1866912193646166016 |
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| author | Nguyen, Duy Nguyen, Trung T. Nguyen, Cuong V. |
| author_facet | Nguyen, Duy Nguyen, Trung T. Nguyen, Cuong V. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_10848 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fake Advertisements Detection Using Automated Multimodal Learning: A Case Study for Vietnamese Real Estate Data Nguyen, Duy Nguyen, Trung T. Nguyen, Cuong V. Machine Learning Artificial Intelligence 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. |
| title | Fake Advertisements Detection Using Automated Multimodal Learning: A Case Study for Vietnamese Real Estate Data |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2501.10848 |