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<p>Recruitment is a crucial part of human resources for businesses and organizations. The growth of online recruitment websites has made them a popular way to find potential candidates. However, this increase has also led to a rise in fraudulent job postings, which can result in losses of personal information, assets, and company reputation. As a result, with advancements in AI, there is a need for tools to evaluate the verification of job postings. Hence, in this paper, we introduce the <b>ViReCAX</b> (<b>Vi</b>etnamese <b>Rec</b>ruiment - <b>A</b>bnormal - <b>Ex</b>planation) dataset, a novel dataset designed for detecting abnormal job postings on Vietnamese job hunter platforms, with an emphasis on providing explanations for the model’s decisions. The dataset comprises 12,054 manually annotated job postings, covering three tasks: job posting classification (CLEAN, WARNING, SEEDING), aspect-level verification (POSITIVE, NEGATIVE, NEUTRAL, NOT-MENTIONED), and explanation generation. Inter-annotator agreement scores were moderate for the classification tasks (0.6 and 0.59) and high for the text generation task (BLEU-2 score of 0.74 and BERTScore of 0.73). We also propose baseline models for detecting abnormal job postings, utilizing BERT-based models with LSTM or CNN for classification tasks and sequence-to-sequence models for the generation task. Finally, we propose three different online streaming learning strategies to improve the model’s ability to adapt to streaming data scenarios.</p>

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Abnormal job postings detection with explanation on Vietnamese job hunter platforms

  • 期刊论文

  • 发表日期：

    2026年5月29日

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  • Vuong-Quoc Huynh Nguyen,
  • Bao-Khanh Phuoc Truong,
  • Anh Hoang Tran,
  • Duong Tien Pham,
  • Son T. Luu,
  • Trong-Hop Do

摘要

Recruitment is a crucial part of human resources for businesses and organizations. The growth of online recruitment websites has made them a popular way to find potential candidates. However, this increase has also led to a rise in fraudulent job postings, which can result in losses of personal information, assets, and company reputation. As a result, with advancements in AI, there is a need for tools to evaluate the verification of job postings. Hence, in this paper, we introduce the ViReCAX (Vietnamese Recruiment - Abnormal - Explanation) dataset, a novel dataset designed for detecting abnormal job postings on Vietnamese job hunter platforms, with an emphasis on providing explanations for the model’s decisions. The dataset comprises 12,054 manually annotated job postings, covering three tasks: job posting classification (CLEAN, WARNING, SEEDING), aspect-level verification (POSITIVE, NEGATIVE, NEUTRAL, NOT-MENTIONED), and explanation generation. Inter-annotator agreement scores were moderate for the classification tasks (0.6 and 0.59) and high for the text generation task (BLEU-2 score of 0.74 and BERTScore of 0.73). We also propose baseline models for detecting abnormal job postings, utilizing BERT-based models with LSTM or CNN for classification tasks and sequence-to-sequence models for the generation task. Finally, we propose three different online streaming learning strategies to improve the model’s ability to adapt to streaming data scenarios.