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

Summary

Arabic NLP has many sentiment and emotion datasets, but most label the feeling expressed in the text itself. They rarely record how audiences actually respond, and almost none follow gender discourse over a long period. This paper addresses that gap with the Arabic Women and Society Corpus, also called the Women's Social Empowerment and Wellbeing corpus.

The authors used the CrowdTangle API to gather public Arabic Facebook posts that matched keywords from three thematic clusters:

  • core empowerment vocabulary,
  • social and legal issues such as harassment or family law,
  • public life and identity.

Pages were not hand-picked. Any public page that published a matching post was included. An automated pipeline performed language identification, normalization and metadata cleaning. A three-step anonymization then removed user identifiers, personal names (via NER) and contact details.

Each post carries engagement counts and the six Facebook reaction types. This lets researchers study how communities collectively react to empowerment narratives. CrowdTangle shut down in 2024, so the archive cannot be rebuilt, which gives it lasting value for Arabic NLP and computational social science.

Key points

  • Scale: 252,487 posts from 51,660 pages in 77 countries, covering 19 Nov 2013 to 9 Jul 2024. The posts total more than 267 million user interactions.
  • Structure: 41 variables grouped into 11 categories, including page attributes, audience size at posting, timing, engagement, sponsorship and performance. Reactions cover Love, Angry, Sad, Haha, Care and Wow.
  • Access: The license is CC BY-NC-SA 4.0. Non-commercial academic access requires a data-sharing agreement with ethics/IRB approval, a defined scope and a ban on de-anonymization. The study itself was ruled IRB-exempt.
  • Interpretation caveats: Reactions are ambiguous. For example, Angry may signal outrage at injustice or rejection of the empowerment message, so reactions should be read as aggregate signals rather than stance labels. Dialect-level analysis would need separate dialect identification tools.
  • Other stated limitations: platform and algorithm changes over the decade, the digital divide, and event-driven spikes in engagement.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/3hkcoy9egx54

@inproceedings{zaghouani2026audience,
  title = {{Audience Engagement with Arabic Women's Social Empowerment and Wellbeing: A Decadal Corpus}},
  author = {Zaghouani, Wajdi and Bessghaier, Mabrouka and Biswas, Md. Rafiul and Ibrahim, Shimaa Amer},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/3hkcoy9egx54},
  url = {https://doi.org/10.63317/3hkcoy9egx54}
}

References

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