# Fake Advertisements Detection Using Automated Multimodal Learning: A Case Study for Vietnamese Real Estate Data

arXiv: https://arxiv.org/abs/2501.10848 · HTML: https://mlsanity.com/paper/2501.10848/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 72 references that are in the map.
- First posted on arXiv 18 Jan 2025.

## Builds on (most cited first, 20 of 20)

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- [Vision-based Real Estate Price Estimation](https://mlsanity.com/paper/1707.05489/) (2017)
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- [Lifelong Property Price Prediction: A Case Study for the Toronto Real Estate Market](https://mlsanity.com/paper/2008.05880/) (2020)
- [Towards Explainable Real Estate Valuation via Evolutionary Algorithms](https://mlsanity.com/paper/2110.05116/) (2021)

## Abstract (arXiv)

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.