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Uncovering Conspiratorial Narratives within Arabic Online Content

Summary

Most computational work on conspiracy theories looks at English-language or Western material, or uses surveys rather than online text. This paper turns to Arabic. The authors built a collection of 2,048 Arabic posts written in both Modern Standard Arabic and dialects. The posts come from public Facebook pages and groups and from one Arabic blog, and span June 2010 to March 2023.

After cleaning and Arabic-specific normalization, they used the Top2Vec algorithm to group texts into themes. They also ran Named Entity Recognition to find the actors each theme mentions, and tracked how themes shift over time. Six families of narratives emerged:

  • gender/feminist
  • geopolitical
  • government cover-ups
  • apocalyptic
  • Judeo-Masonic
  • geoengineering

The authors read these themes against regional history, culture and political instability. They argue that spreading such stories can work as a form of protest, and that the narratives adapt to current events. The study is exploratory rather than a benchmark. Its value is a first map of Arabic conspiracist discourse on platforms other than Twitter, combining topic and entity analysis.

Key points

  • Corpus: 2,048 Arabic texts in MSA and dialectal Arabic, from Facebook public pages/groups plus one Arabic blog. The posts cover June 2010 to March 2023 and span politics, science, economics, society, entertainment and culture.
  • Cleaning: The authors removed uninformative documents, texts under five words, duplicates, and items containing only URLs or symbols. They then normalized Arabic words, stripped diacritics and punctuation, and filtered stop words.
  • Methods: The pipeline combines Top2Vec topic modeling, Named Entity Recognition and temporal analysis of dominant themes.
  • Result: Six narrative categories were found, with notable weight on geopolitical dynamics and powerful entities.
  • Limitations and future work: The authors admit that topic models and NER miss much of the discourse's depth. They call for Arabic-tailored NER/TM, study of rhetoric and word choice, and cross-cultural comparison.

Interesting ideas

  • I1. Linking each discovered theme to the named entities it mentions turns a vague cluster into a list of who the narrative is about, which makes topics easier to interpret and check. — For Mova (world): Each entity in a text's world could carry the themes or frames it takes part in, so an answer can explain which actors drive a given storyline.
  • I2. Slicing themes by time period shows how a narrative drifts and which events trigger it, instead of treating the corpus as one static snapshot. — For Mova (learn): Evaluation sets could be split by source date to detect whether parser or frame accuracy degrades on newer text, acting as a drift control.
  • I3. Cheap, explicit cleaning rules (minimum length, deduplication, dropping URL-only items) remove much noise before any modeling. Every exclusion stays auditable. — For Mova (learn): The same transparent filters could screen silver data before witness parsers vote, with a log of why each sentence was rejected.
  • I4. The paper's working definition treats a conspiracy theory as blaming events on a hidden plot by a powerful group against the common good. That is essentially a structured template with agent, hidden intent and victim. — For Mova (frames): This could become a pragmatic frame that flags texts attributing covert harmful agency to a group, distinguishing claimed intent from asserted fact.
  • I5. Interpreting clusters through cultural and historical context, rather than accepting raw topic labels, guards against reading regional discourse through a Western lens. — For Mova (answer): QA on tales and texts from other cultures could include a context check, so answers note when an interpretation depends on background knowledge the text assumes.

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

Topics: Named entity recognition · Discourse analysis

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2504.14037 · arXiv 2504.14037

@misc{mohdeb2025uncovering,
  title = {{Uncovering Conspiratorial Narratives within Arabic Online Content}},
  author = {Mohdeb, Djamila and Laifa, Meriem and Guemraoui, Zineb and Behih, Dalila},
  year = {2025},
  doi = {10.48550/arxiv.2504.14037},
  eprint = {2504.14037},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2504.14037}
}

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