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This research introduces Centroid-Guided Contrastive Loss (CGCL), a novel loss function designed to improve the detection of fraudulent job postings by unifying classification with dense clustering in latent space. By employing a centroid-driven push-and-pull mechanism, the method achieves state-of-the-art accuracy while preserving meaningful structural representations that capture subtle nuances among fake posts. The approach addresses a critical gap in current models that struggle to balance high decision boundary precision with compact latent-space organization.
Beyond Accuracy: Centroid-Guided Contrastive Loss for Structured Fraudulent Job Posting Detection
arXiv:2609.21599v1 Announce Type: new Abstract: Fraudulent job posting detection aims to identify job advertisements that are corrupted either through fake content, misleading information, or negative intent, disrupting the online eco-system of job-seekers and employers. Existing studies in this domain lack effective methods to simultaneously achieve high accuracy and meaningful structure of latent-space representations that capture subtleties among fake posts. To this end, we propose Centroid-Guided Contrastive Loss (CGCL), a loss function which unifies classification with densely formulated clustering to consistently reshape latent-space through a centroid-driven top-$k$ push-and-pull mechanism. The complementary nature of CGCL enables the model to enforce accurate decision boundaries and maintain high clustering compactness, effectively capturing both class separability and latent structure. Extensive experiments demonstrate the state-of-the-art (SOTA) performance of our method on EMSCAD, a public benchmark dataset. The code associated with this work is available at: https://github.com/ali-ahmed925/CGCL_code
Abstract:Fraudulent job posting detection aims to identify job advertisements that are corrupted either through fake content, misleading information, or negative intent, disrupting the online eco-system of job-seekers and employers. Existing studies in this domain lack effective methods to simultaneously achieve high accuracy and meaningful structure of latent-space representations that capture subtleties among fake posts. To this end, we propose Centroid-Guided Contrastive Loss (CGCL), a loss function which unifies classification with densely formulated clustering to consistently reshape latent-space through a centroid-driven top-$k$ push-and-pull mechanism. The complementary nature of CGCL enables the model to enforce accurate decision boundaries and maintain high clustering compactness, effectively capturing both class separability and latent structure. Extensive experiments demonstrate the state-of-the-art (SOTA) performance of our method on EMSCAD, a public benchmark dataset. The code associated with this work is available at: this https URL
12 pages, 6 figures, 8 tables. Submitted to IEEE Open Journal of the Computer Society. Code: this https URL
Artificial Intelligence (cs.AI)
arXiv:2609.21599 [cs.AI]
(or arXiv:2609.21599v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.21599 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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