--- title: "From Posts to Pressure — paper by Zaghouani et al. (2026)" description: "The authors address this by building AraStress, which they describe as the first large-scale Arabic corpus… 1 reference, cited by 3 papers here." authors: ["Wajdi Zaghouani", "Eman Sedqy Shlkamy", "Mabrouka Bessghaier"] year: 2026 venue: "Proceedings of the 2nd Workshop on NLP for Languages Using Arabic Script" source: "https://aclanthology.org/2026.abjadnlp-1.50/" doi: "10.18653/v1/2026.abjadnlp-1.50" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/zaghouani-2026-posts-pressure-arabic-dataset-about/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/zaghouani-2026-posts-pressure-arabic-dataset-about/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/4/) / From Posts to Pressure: An Arabic Dataset about Stress and Mental-Health Monitoring # From Posts to Pressure: An Arabic Dataset about Stress and Mental-Health Monitoring **Authors:** [Wajdi Zaghouani](https://md.research.elamur.ai/free/en/authors/wajdi-zaghouani/), Eman Sedqy Shlkamy and [Mabrouka Bessghaier](https://md.research.elamur.ai/free/en/authors/mabrouka-bessghaier/) · **Year:** 2026 · **Venue:** [Proceedings of the 2nd Workshop on NLP for Languages Using Arabic Script](https://md.research.elamur.ai/free/en/venues/abjadnlp/) ([Workshop on NLP for Languages Using Arabic Script 2026](https://md.research.elamur.ai/free/en/venues/abjadnlp/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2026.abjadnlp-1.50/) · [PDF](https://aclanthology.org/2026.abjadnlp-1.50.pdf) · [DOI 10.18653/v1/2026.abjadnlp-1.50](https://doi.org/10.18653/v1/2026.abjadnlp-1.50) · [OpenAlex](https://openalex.org/W7140123968) **Citations:** 1 citations (OpenAlex, 2026-10-14) · [3](https://md.research.elamur.ai/free/en/papers/zaghouani-2026-posts-pressure-arabic-dataset-about/cited-by/) in our library, of the 149,439 papers whose bibliographies we read ## Summary Arabic mental-health NLP has concentrated mostly on depression and suicidal ideation, usually with data from X. Psychological stress has received little attention, and Facebook, one of the most popular platforms in the Arab world, has been largely ignored. The authors address this by building AraStress, which they describe as the first large-scale Arabic corpus focused on stress. It contains 175,862 public posts collected through CrowdTangle from mental-health and wellness pages using stress-related keywords. The posts cover the pandemic and the years after it, and each one comes with detailed engagement, temporal, geographic and page-type metadata. The analysis combines normalized reaction ratios, two small lexicons (religious and therapeutic), and a proportional comparison against a separate Arabic Trauma Corpus. Responses to stress posts are mostly warm or humorous, with very little anger. Stress tends to be told as personal narrative that blends faith-based and clinical vocabulary, and this mix varies by country. Trauma posts draw noticeably more sadness. The resource supports culturally sensitive stress-detection models for Arabic speakers. ## Key points - **AraStress:** 175,862 public Arabic Facebook posts collected between January 2020 and May 2024 via CrowdTangle. Each post has 52 metadata fields, including separate counts for each reaction type, comments, shares, timestamps, Page Admin Top Country, and page category. Access is for research only, through an anonymized release or on request. - **Reactions to stress posts:** Love 60.7%, Haha 17.7%, Sad 11.6%, Care 6.3%, Angry 1.8%. The authors read this as a mix of empathy and humor used for coping. Virality was defined as engagement above the 90th percentile. - **Comparison corpus:** the Arabic Trauma Corpus has 16,529 Facebook posts (May 2014 to May 2024) with 551,904 reactions. Its reactions are Love 51.4%, Sad 22.5% and Haha 19.7%, so sadness is roughly twice as common as in the stress data. - **Lexical framing:** "God" accounts for about 75% of religious-lexicon hits (49,761 occurrences). "Treatment/therapy" is the leading therapeutic term (23,875 occurrences, about 34%). The ratio of religious to therapeutic vocabulary differs across countries. - **Limitations:** country labels come from page administrators, not from the individual users who wrote the posts. Reactions reflect user behavior rather than clinical assessment. The lexicon-based method cannot tell supportive humor from sarcasm. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* 📚 Shelves (5): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [💭 Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/) · 1,649 works · [💭 Experimental and Cognitive Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/) · 930 works · [💭 Social Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/social-psychology/) · 330 works 🧭 Topics (1): [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · 3,066 works ## Cite Cite as: Wajdi Zaghouani, Eman Sedqy Shlkamy and Mabrouka Bessghaier. 2026. From Posts to Pressure: An Arabic Dataset about Stress and Mental-Health Monitoring. Proceedings of the 2nd Workshop on NLP for Languages Using Arabic Script. The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{zaghouani2026posts, title = {{From Posts to Pressure: An Arabic Dataset about Stress and Mental-Health Monitoring}}, author = {Wajdi Zaghouani, Eman Sedqy Shlkamy and Mabrouka Bessghaier}, booktitle = {Proceedings of the 2nd Workshop on NLP for Languages Using Arabic Script}, year = {2026}, doi = {10.18653/v1/2026.abjadnlp-1.50}, url = {https://doi.org/10.18653/v1/2026.abjadnlp-1.50} } ``` - [← previous Framing Political Bias in Multilingual LLMs Across Pakistani Languages](https://md.research.elamur.ai/free/en/papers/nadeem-2026-framing-political-bias-multilingual-llms/) - [next → Gated Differentiable Working Memory for Long-Context Language Modeling](https://md.research.elamur.ai/free/en/papers/mei-2026-gated-differentiable-working-memory-long/) - [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/4/) ## References 1 references, 1 of them with a card of ours. ↗ leads to the work. 1. Asma Abdulsalam, Areej Alhothali, and Saleh Al-Ghamdi. 2024. [Detecting suicidality in arabic tweets using machine learning and deep learning techniques](https://md.research.elamur.ai/free/en/papers/abdulsalam-2023-detecting-suicidality-arabic-tweets-using/). Arabian Journal for Science and Engineering, 49(9):12729– 12742. [↗](https://doi.org/10.1007/s13369-024-08767-3) *Updated 1 November 2026*