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

TL;DR

This paper introduces FADAML, an automated multimodal machine learning system designed to detect fake online advertisements, demonstrated on Vietnamese real estate data with high accuracy.

Contribution

The paper presents a novel end-to-end system combining multimodal and automated machine learning techniques for fake advertisement detection.

Findings

Achieved 91.5% detection accuracy on Vietnamese real estate ads.

Outperformed three state-of-the-art fake news detection systems.

Validated effectiveness of multimodal learning in fake ad detection.

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.