Fake advertisements detection using automated multimodal learning: a case study for Vietnamese real estate data

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Fake advertisements detection using automated multimodal learning: a case study for Vietnamese real estate data

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Fake advertisements detection using automated multimodal learning: a case study for Vietnamese real estate data

Popis výsledku anglicky

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.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Applied Intelligence

ISSN

0924-669X

e-ISSN

—

Svazek periodika

55

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

8

Strana od-do

367

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85217773257