# ArabJobs: A Multinational Corpus of Arabic Job Ads

arXiv: https://arxiv.org/abs/2509.22589 · HTML: https://mlsanity.com/paper/2509.22589/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 14 references that are in the map.
- First posted on arXiv 26 Sep 2025.

## Builds on (most cited first, 8 of 8)

- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [NADI 2020: The First Nuanced Arabic Dialect Identification Shared Task](https://mlsanity.com/paper/2010.11334/) (2020)
- [ALDi: Quantifying the Arabic Level of Dialectness of Text](https://mlsanity.com/paper/2310.13747/) (2023)
- [AraWEAT: Multidimensional Analysis of Biases in Arabic Word Embeddings](https://mlsanity.com/paper/2011.01575/) (2020)
- [MedArabiQ: Benchmarking Large Language Models on Arabic Medical Tasks](https://mlsanity.com/paper/2505.03427/) (2025)
- [The Arabic Parallel Gender Corpus 2.0: Extensions and Analyses](https://mlsanity.com/paper/2110.09216/) (2021)
- [The SAMER Arabic Text Simplification Corpus](https://mlsanity.com/paper/2404.18615/) (2024)
- [A Large and Balanced Corpus for Fine-grained Arabic Readability Assessment](https://mlsanity.com/paper/2502.13520/) (2025)

## Built on by (most cited first, 3 of 3)

- [VietJobs: A Vietnamese Job Advertisement Dataset](https://mlsanity.com/paper/2603.05262/) (2026)
- [JobArabi: An Arabic Corpus and Analysis of Job Announcements from Social Media](https://mlsanity.com/paper/2605.20960/) (2026)
- [AraGenre 2026: A Hierarchical Definition-Guided Arabic Genre Classification Shared Task](https://mlsanity.com/paper/2609.27387/) (2026)

## Abstract (arXiv)

ArabJobs is a publicly available corpus of Arabic job advertisements collected from Egypt, Jordan, Saudi Arabia, and the United Arab Emirates. Comprising over 8,500 postings and more than 550,000 words, the dataset captures linguistic, regional, and socio-economic variation in the Arab labour market. We present analyses of gender representation and occupational structure, and highlight dialectal variation across ads, which offers opportunities for future research. We also demonstrate applications such as salary estimation and job category normalisation using large language models, alongside benchmark tasks for gender bias detection and profession classification. The findings show the utility of ArabJobs for fairness-aware Arabic NLP and labour market research. The dataset is publicly available on GitHub: https://github.com/drelhaj/ArabJobs.