Fake Job Postings Dataset - Replication Archive for "TabuLLM: Feature Extraction from Tabular Text Data using Large Language Models"

This dataset contains 17,880 job postings labeled as fraudulent (4.84%) or legitimate (95.16%). It is the EMSCAD (Employment Scam Aegean Dataset) originally published by Vidros et al. (2017, Future Internet, doi:10.3390/fi9010006) and distributed via Kaggle (shivamb/real-or-fake-fake-jobposting-prediction). This archive is provided for reviewers and readers of the manuscript "TabuLLM: Feature Extraction from Tabular Text Data using Large Language Models" (submitted to the Journal of Statistical Software), as a convenience mirror to support offline replication without requiring a Kaggle account. File: fake_job_postings.csv - 17,880 rows × 18 columns. Columns include 7 free-text fields (title, location, department, company_profile, description, requirements, benefits), 3 binary indicators, 5 categorical features, 1 numeric identifier (job_id), 1 sparse field (salary_range, 84% missing), and 1 binary target (fraudulent). Version 2 adds pre-computed text embeddings for the 7 free-text columns (title, location, department, company_profile, description, requirements, benefits), generated using the OpenAI Embeddings API (May 2026). Each zip contains 7 NumPy .npy files, one per column, in the order listed above. fraud_openai-small_zenodo.zip — model text-embedding-3-small (1536 dimensions); each file shape: (17880, 1536), dtype float64. fraud_openai-large_zenodo.zip — model text-embedding-3-large (3072 dimensions); each file shape: (17880, 3072), dtype float64. File naming convention: fraud_{model}_{dim}d_{column}.npy. Row order matches fake_job_postings.csv exactly.