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Analyzing Politeness in Arabic Tweets: A Preliminary Study

Summary

The authors address automatic politeness detection in Arabic social media, a task that has received little computational attention compared with English. They built a small corpus of 500 tweets taken from the ArSarcasm dataset, keeping only items tagged as MSA with positive sentiment. A linguistic expert manually labelled each tweet as polite or neutral, based on two criteria: lexical politeness markers and the pragmatic intent behind the message, such as appreciation, respect, love or admiration.

Two families of methods were compared. The first fine-tunes the Arabic encoders MarBERT and CamelBERT as baselines. The second uses zero-shot and few-shot prompting with three large language models: GPT-4o-mini, JAIS 30B Chat and Cohere Command. JAIS 30B Chat and GPT-4o-mini clearly outperformed Cohere Command, and adding more demonstrations did not improve results.

Error analysis showed that the models tend to over-predict politeness, miss the intent of a whole sentence, and are misled by emojis. As an early benchmark, the study highlights how much culture-aware resources matter for Arabic pragmatics.

Key points

  • Data and labels: 500 MSA tweets with positive sentiment from ArSarcasm, labelled for binary politeness (polite vs. neutral) using lexical markers plus pragmatic intention.
  • 7-shot results: JAIS 30B Chat ranked first, GPT-4o-mini reached 70.73% accuracy, and Cohere Command reached 59.15%.
  • 20-shot results: accuracy dropped slightly for all three models. JAIS 30B Chat scored 67.59%, GPT-4o-mini 66.39% and Cohere Command 58.87%.
  • Fine-tuned baselines: MarBERT labelled every tweet as polite, a degenerate output. CamelBERT matched the manual labels except for about 12% of cases.
  • Stated limitations: the corpus is small and MSA-only, a single annotator introduces subjectivity, and binary labels may oversimplify politeness. Planned future work includes larger datasets and the integration of cultural and pragmatic knowledge.

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

Cited by

4 papers of our library cite it (of the 140673 whose bibliographies we read); 1 of them have a page here, 3 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work.

@inproceedings{alkhalifa2024analyzing,
  title = {{Analyzing Politeness in Arabic Tweets: A Preliminary Study}},
  author = {Al-Khalifa, Hend and Ghezaiel, Nadia and Bounnit, Maria},
  booktitle = {Proceedings of the 7th International Conference on Natural Language and Speech Processing (ICNLSP 2024)},
  year = {2024},
  url = {https://aclanthology.org/2024.icnlsp-1.36/}
}

References

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