agnialf/Internship-Job-Scam-Dataset

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.

Dataset Summary

The dataset contains job posting texts labeled into three risk categories:

• 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

The final dataset contains 900 examples: 300 legitimate, 300 suspicious, and 300 fraudulent job postings.

Source Data

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).

Sources:

• Kaggle: shivamb/real-or-fake-fake-jobposting-prediction
• Original dataset: Employment Scam Aegean Dataset (EMSCAD)

Custom Processing

The original dataset contains job advertisements with a binary fraudulent label. I transformed it into a custom internship-focused dataset by:

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.

Columns

The dataset includes:

• 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

Limitations

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.

References:

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.