Fake Advertisements Detection Using Automated Multimodal Learning: A Case Study for Vietnamese Real Estate Data
This addresses the issue of fake ads for e-commerce users and platforms, but it is incremental as it adapts existing techniques to a specific domain.
The paper tackled the problem of detecting fake advertisements on e-commerce platforms, specifically applying a system called FADAML to Vietnamese real estate data, achieving 91.5% detection accuracy and outperforming state-of-the-art methods.
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.