Fake advertisements detection using automated multimodal learning: a case study for Vietnamese real estate data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3ALKPC94HI" target="_blank" >RIV/00216208:11320/26:LKPC94HI - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/s10489-025-06238-2" target="_blank" >http://dx.doi.org/10.1007/s10489-025-06238-2</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10489-025-06238-2" target="_blank" >10.1007/s10489-025-06238-2</a>
Alternative languages
Result language
angličtina
Original language name
Fake advertisements detection using automated multimodal learning: a case study for Vietnamese real estate data
Original language description
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. © The Author(s) 2025.
Czech name
—
Czech description
—
Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
—
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Applied Intelligence
ISSN
0924-669X
e-ISSN
—
Volume of the periodical
55
Issue of the periodical within the volume
6
Country of publishing house
US - UNITED STATES
Number of pages
8
Pages from-to
367
UT code for WoS article
—
EID of the result in the Scopus database
2-s2.0-85217773257