--- title: "Analyzing Politeness in Arabic Tweets — Al-Khalifa 2024" description: "The authors address automatic politeness detection in Arabic social media, a task that has received little computational attention compared with English." authors: ["Hend Al-Khalifa", "Nadia Ghezaiel", "Maria Bounnit"] year: 2024 venue: "Proceedings of the 7th International Conference on Natural Language and Speech Processing (ICNLSP 2024)" source: "https://aclanthology.org/2024.icnlsp-1.36/" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/papers/khalifa-2024-analyzing-politeness-arabic-tweets-preliminary/" --- [Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/khalifa-2024-analyzing-politeness-arabic-tweets-preliminary/) [home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2024](https://md.research.elamur.ai/free/papers/2024/) / Analyzing Politeness in Arabic Tweets: A Preliminary Study # Analyzing Politeness in Arabic Tweets: A Preliminary Study **Authors:** [Hend Al-Khalifa](https://md.research.elamur.ai/free/authors/hend-al-khalifa/), Nadia Ghezaiel, Maria Bounnit · **Year:** 2024 · **Venue:** Proceedings of the 7th International Conference on Natural Language and Speech Processing (ICNLSP 2024) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2024.icnlsp-1.36/) · [PDF](https://aclanthology.org/2024.icnlsp-1.36.pdf) **Citations:** [4](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## 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](https://md.research.elamur.ai/free/shelves/linguistics/) ## Cited by 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. - [GHAD NLP at AdabEval2026: Transformer-Based Approach for Arabic Politeness and Pragmatic Category Classification](https://md.research.elamur.ai/free/papers/alfattni-2026-ghad-nlp-adabeval2026-transformer-based/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @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/} } ``` - [← previous An Image is Worth 1/2 Tokens After Layer 2: Plug-and-Play Inference Acceleration for Large Vision-Language Models](https://md.research.elamur.ai/free/papers/chen-2024-image-worth-1-2-tokens/) - [next → Ancient but Digitized: Developing Handwritten Optical Character Recognition for East Syriac Script Through Creating KHAMIS Dataset](https://md.research.elamur.ai/free/papers/langstream-2024-majeed-ancient-but-digitized-developing-handwritten/) - [↑ back to papers of 2024](https://md.research.elamur.ai/free/papers/2024/) ## References 19 references, 6 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. 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