Abstract
Fraudulent jobs are an emerging threat over online recruitment platforms such as LinkedIn, Glassdoor. Fraudulent job postings affect the platform’s trustworthiness and have a negative impact on user experience. Therefore, these platforms need to detect and remove these fraudulent jobs. Generally, fraudulent job postings contain untenable facts about domain-specific entities such as mismatch in skills, industries, offered compensation, etc. However, existing approaches focus on studying writing styles, linguistics, and context-based features, and ignore the relationships among domain-specific entities. To bridge this gap, we propose an approach based on the Knowledge Graph (KG) of domain-specific entities to detect fraudulent jobs. In this paper, we present a multi-tier novel end-to-end framework called FRaudulent Jobs Detection (FRJD) Engine, which considers a) fact validation module using KGs, b) contextual module using deep neural networks c) meta-data module to capture the semantics of job postings. We conduct our experiments using a fact validation dataset containing 4 million facts extracted from job postings. Extensive evaluation shows that FRJD yields a 0.96 F1-score on the curated dataset of 157,880 job postings. Finally, we provide insights on the performance of different fact-checking algorithms on recruitment domain datasets.
P. Kumaraguru—Major part of this work was done while Ponnurangam Kumaraguru was a faculty at IIIT-Delhi.
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Acknowledgements
We would like to acknowledge the support from SERB, InfoEdge India Limited, and FICCI. We are grateful to PreCog Research Group and Dr. Siddartha Asthana for critically reviewing the manuscript and stimulating discussions.
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Goyal, N., Sachdeva, N., Kumaraguru, P. (2021). Spy the Lie: Fraudulent Jobs Detection in Recruitment Domain using Knowledge Graphs. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management . KSEM 2021. Lecture Notes in Computer Science(), vol 12816. Springer, Cham. https://doi.org/10.1007/978-3-030-82147-0_50
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