用多模态自动学习检测越南房产虚假广告，准确率达91.5%
Fake Advertisements Detection Using Automated Multimodal Learning: A Case Study for Vietnamese Real Estate Data
- 融合多模态与自动化机器学习的端到端检测系统
- 在越南房产数据上实现91.5%的检测准确率
- 适合电商反欺诈与内容安全团队参考
电商平台兴起带来虚假广告问题，用户面临财务与数据风险，平台声誉也受损害。本文提出FADAML，一种新型端到端机器学习系统，用于检测并过滤虚假在线广告。该系统结合多模态机器学习与自动化机器学习技术，显著提升检测效果。以越南主流房产网站为案例研究，实验表明其检测准确率达91.5%，显著优于三种现有先进虚假新闻检测系统。
原文摘要 · Abstract (English)
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.
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