| This dataset is a custom-built text classification dataset for detecting fraud risk in internship and entry-level job postings. This dataset is created for an embeddings-based classifier that helps students evaluate whether internship and entry-level job postings may be legitimate, suspicious, or fraudulent. |
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| ## Dataset Summary |
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| The dataset contains job posting texts labeled into three risk categories: |
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| - `legitimate`: postings that look like normal internship or entry-level job advertisements |
| - `suspicious`: postings with warning signs such as vague requirements, missing company information, remote-only work, unclear application process, or unrealistic opportunity language |
| - `fraudulent`: postings originally labeled as fraudulent in the source dataset |
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| The final dataset contains 900 examples: 300 legitimate, 300 suspicious, and 300 fraudulent job postings. |
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| ## Source Data |
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| This dataset was adopted from the Kaggle public dataset: Real / Fake Job Posting Prediction dataset, which is based on the Employment Scam Aegean Dataset (EMSCAD). |
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| Sources: |
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| - Kaggle: `shivamb/real-or-fake-fake-jobposting-prediction` |
| - Original dataset: Employment Scam Aegean Dataset (EMSCAD) |
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| ## Custom Processing |
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| The original dataset contains job advertisements with a binary `fraudulent` label. I transformed it into a custom internship-focused dataset by: |
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| 1. Combining job fields such as title, location, company profile, description, requirements, benefits, employment type, education, industry, and function into one text field. |
| 2. Filtering toward internship, student, trainee, junior, assistant, graduate, and entry-level roles. |
| 3. Preserving fraudulent examples so the model learns scam-related language. |
| 4. Converting the original binary label into three labels: `legitimate`, `suspicious`, and `fraudulent`. |
| 5. Adding red-flag annotations such as missing company profile, missing requirements, no company logo, no screening questions, remote/telecommuting, money-transfer language, upfront-fee language, and unrealistic easy-money language. |
| 6. Creating transparent rule-based suspicious variants from legitimate examples to balance the borderline-risk class. |
| 7. Balancing the final dataset to 300 examples per class. |
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| ## Columns |
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| The dataset includes: |
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| - `id`: row identifier |
| - `source_job_id`: original job ID or generated suspicious variant ID |
| - `title`: job title |
| - `location`: job location |
| - `text`: combined job posting text used for model training |
| - `label`: target class (`legitimate`, `suspicious`, `fraudulent`) |
| - `risk_level`: low, medium, or high |
| - `red_flags`: semicolon-separated warning indicators |
| - `source_dataset`: source dataset name |
| - `source_type`: processed public dataset or rule-augmented suspicious variant |
| - `original_fraudulent`: original binary fraud label from EMSCAD |
| - `internship_related`: whether the posting matched internship/early-career terms |
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| ## Limitations |
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| This dataset should not be used as a final authority for deciding whether a job is safe. The `suspicious` class is partly rule-based and may not capture all real-world borderline cases. The dataset is useful for experimentation, model comparison, and demo development, but users should still manually verify company identity, application links, contact methods, and payment-related requests. |
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| ## References: |
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| Vidros, S., Kolias, C., Kambourakis, G., & Akoglu, L. (2017). Automatic detection of online recruitment frauds: Characteristics, methods, and a public dataset. *Future Internet, 9*(1), 6. |
