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

2026-05-21

AI Summary

This study addresses the scarcity of large-scale, structured Arabic social media data for investigating public engagement and sentiment related to women’s empowerment and societal well-being. To this end, we present a novel corpus spanning 2013–2024, compiled from over 50,000 Facebook pages across 77 countries, comprising more than 250,000 posts and over 267 million user interactions. The corpus uniquely integrates multiple Arabic dialects, user engagement metrics, and sentiment annotations. An automated pipeline ensures high data quality and reproducibility through language identification, text normalization, and metadata cleaning. This resource constitutes a rare and valuable asset for research in Arabic natural language processing, computational social science, and digital communication, and will be made openly available for academic use.

Abstract

This paper presents the Arabic Women and Society Corpus, a ten year collection of 252,487 public Arabic Facebook posts related to women's empowerment and social wellbeing. The corpus was collected from 51,660 pages across 77 countries between 2013 and 2024, resulting in more than 267 million user interactions. Each post includes engagement metrics such as shares, comments, and emotional reactions, providing a unique view of audience sentiment and social attention. The data were processed using an automated pipeline with language identification, normalization, and metadata cleaning to ensure reliability and reproducibility. The corpus enables large scale analysis of gender discourse, social reform, and emotional engagement across Arabic dialects. It supports research in Arabic natural language processing, computational social science, and digital communication studies. The dataset and accompanying documentation will be released under request for research use.

Problem

• Arabic women
• social empowerment
• audience engagement
• wellbeing
• social media corpus

Innovation

• Arabic NLP
• social media corpus
• audience engagement
• computational social science
• gender discourse