Audience Engagement with Arabic Women's Social Empowerment and Wellbeing: A Decadal Corpus

2026-05-22 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

The Arabic Women and Society Corpus is a newly presented dataset comprising 252,487 public Arabic Facebook posts focused on women's empowerment and social wellbeing. Collected over a decade, from 2013 to 2024, this corpus aggregates data from 51,660 pages across 77 countries, capturing more than 267 million user interactions. Each post includes engagement metrics such as shares, comments, and emotional reactions, offering a unique perspective on audience sentiment and social attention. The data was meticulously processed using an automated pipeline for language identification, normalization, and metadata cleaning, ensuring its reliability. This corpus facilitates large-scale analysis of gender discourse, social reform, and emotional engagement across various Arabic dialects, supporting research in Arabic natural language processing, computational social science, and digital communication studies. The dataset will be made available for research use upon request.

Key takeaway

For research scientists and NLP engineers focused on Arabic language and social dynamics, this corpus offers a critical resource. If you are investigating women's empowerment, social wellbeing, or gender discourse in the Arab world, requesting access to this decadal Facebook post collection will provide rich, engagement-metric data. Utilize it to conduct large-scale analyses, validate models, or explore emotional engagement trends across diverse Arabic dialects.

Key insights

The corpus offers a decadal view of Arabic audience engagement with women's empowerment and wellbeing via Facebook posts and interactions.

Method

The corpus was collected via an automated pipeline involving language identification, normalization, and metadata cleaning to ensure data reliability and reproducibility.

In practice

• Analyze gender discourse.
• Study social reform trends.
• Support Arabic NLP research.

Topics

• Arabic NLP
• Social Empowerment
• Women's Wellbeing
• Computational Social Science
• Facebook Data
• Gender Discourse

Best for: AI Scientist, NLP Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.