RT Article
T1 Cyber victimization in hybrid space: an analysis of employment scams using natural language processing and machine learning models
JF Journal of crime and justice
VO 49
IS 1
SP 132
OP 153
A1 Gong, Wenjing
A1 Lee, Claire Seungeun
A1 Li, Shoujia
A1 Adkison, Daylon
A1 Li, Na
A1 Wu, Ling
A1 Ye, Xinyue
A2 Lee, Claire Seungeun
A2 Li, Shoujia
A2 Adkison, Daylon
A2 Li, Na
A2 Wu, Ling
A2 Ye, Xinyue
LA English
YR 2026
UL https://krimdok.uni-tuebingen.de/Record/195004825X
AB Employment scams are a growing and serious issue, increasingly targeting job seekers and exploiting vulnerabilities in the online recruitment process. Current employment scam studies focus primarily on virtual spaces during job searches, neglecting the impact of hybrid spaces (physical and virtual spaces combined) on cyber victimization. This study, leveraging advancements in artificial intelligence, assessed victimization and developed mechanisms to help job seekers reduce their risk of employment scams, considering the physical locations in the real world, as well as the virtual locations described in the job postings. The results show that the consistency of geographic information in the hybrid space of fake job postings is lower than that of legitimate job postings, and there is spatial heterogeneity in the distribution of the physical locations of fake job postings. This consistency, as well as detailed physical location, contributes significantly to the identification and classification of genuine and fake postings. Integrating multiple disciplines, this research enhances understanding of the prevalence, impact, contributing factors, and mitigation strategies associated with cyber victimization during employment. It also contributes to the development of novel methodologies and approaches for detecting, mitigating, and preventing cybercrime.
K1 Machine Learning
K1 hybrid space
K1 employment scams
K1 Cyber victimization
DO 10.1080/0735648X.2024.2448804