--- title: "Investigating Persuasion Techniques in — Alzahrani 2024" description: "The paper looks at automatically spotting persuasion techniques in Arabic social media posts." authors: ["Abdurahmman Alzahrani", "Eyad Babkier", "Faisal Yanbaawi", "Firas Yanbaawi", "Hassan Alhuzali"] year: 2024 venue: "arXiv:2405.12884" source: "https://arxiv.org/abs/2405.12884" doi: "10.48550/arxiv.2405.12884" arxiv: "2405.12884" licence: "CC BY-NC-ND 4.0 (arXiv)" licence_url: "http://creativecommons.org/licenses/by-nc-nd/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/alzahrani-2024-investigating-persuasion-techniques-arabic-empirical/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/alzahrani-2024-investigating-persuasion-techniques-arabic-empirical/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2024](https://md.research.elamur.ai/free/en/papers/2024/page/3/) / Investigating Persuasion Techniques in Arabic: An Empirical Study Leveraging Large Language Models # Investigating Persuasion Techniques in Arabic: An Empirical Study Leveraging Large Language Models **Authors:** Abdurahmman Alzahrani, Eyad Babkier, Faisal Yanbaawi, Firas Yanbaawi, Hassan Alhuzali · **Year:** 2024 · **Venue:** arXiv:2405.12884 · **Licence:** [CC BY-NC-ND 4.0 (arXiv)](http://creativecommons.org/licenses/by-nc-nd/4.0/) **Links:** [Publisher's page](https://arxiv.org/abs/2405.12884) · [PDF](https://arxiv.org/pdf/2405.12884) · [DOI 10.48550/arxiv.2405.12884](https://doi.org/10.48550/arxiv.2405.12884) · [arXiv 2405.12884](https://arxiv.org/abs/2405.12884) · [OpenAlex](https://openalex.org/W4398230414) **Citations:** 0 citations (openalex-snapshot · 2026-10-14) · [1](#cited-by) in our library, of the 140884 papers whose bibliographies we read ## Summary The paper looks at automatically spotting persuasion techniques in Arabic social media posts. The authors frame this as a way to counter manipulation and misinformation, and they give Saudi examples such as an invented sick child and false rumours about Mount Uhud. They use the ArAIEval persuasion dataset, which has two subtasks. Task 1A is binary: does a text use any persuasion technique? Task 1B is multi-label: which techniques does it use? Three ways of using pre-trained language models are compared: - **Feature extraction:** frozen Arabic BERT-style encoders supply features. - **Fine-tuning:** the same encoders are trained further on the task. - **Prompting:** GPT-3.5 and GPT-4 are prompted in zero-shot and few-shot modes. Fine-tuning works best, reaching f1-micro 0.865 and f1-weighted 0.861, and GigaBERT is the strongest fine-tuned model. Task 1B scores are a little lower because the multi-label setup is harder. The GPT models do poorly without examples, but adding a handful of labelled demonstrations improves them by up to about 20%. The authors present the study as an exploration of methods rather than a bid for state-of-the-art results. ## Key points - The data is the ArAIEval persuasion technique dataset, with Task 1A (binary presence) and Task 1B (multi-label technique types). - Four encoders are tested both as frozen feature extractors and as fine-tuned models: AraBERTv2, MARBERT, CAMeLBERT and GigaBERT. - In the frozen setting, CAMeLBERT edges out MARBERT and GigaBERT. Once fine-tuned, GigaBERT leads on all metrics, with best scores of f1-micro 0.865 and f1-weighted 0.861. - On Task 1A, GPT-3.5 and GPT-4 score very low zero-shot. With just three labelled examples, their results rise by up to roughly 20%. - Planned future work includes multi-label loss functions designed to handle imbalanced technique labels. ## Interesting ideas - The same set of encoders is run once frozen and once fine-tuned, so the gain from adapting the weights is isolated from the choice of model. — For Mova (learn): Run gparse with a frozen and a fine-tuned transformer encoder on identical treebank splits, so we can tell whether adaptation or the encoder itself drives LAS changes. - Detection is split into a coarse yes/no step ("is any technique present?") and a finer step naming which techniques appear, with the fine step being measurably harder. — For Mova (frames): prag could first flag whether a clause carries a manipulative move (e.g., appeal to emotion) and only then assign its type, keeping each decision separately explainable. - Comparing zero-shot with few-shot prompting using only three demonstrations is a cheap test of how much an LLM depends on examples for a labelling task. — For Mova (learn): Before trusting an LLM teacher tier for silver labels, measure its zero-shot vs three-shot gap on a small gold sample; a big gap signals unstable labels. - Micro and weighted F1 are reported together, and imbalance-aware multi-label losses are named as the next step, because technique labels are skewed. — For Mova (frames): Rare labels such as irony or specific speech acts should be scored with weighted as well as micro metrics, so frequent classes do not mask failures on rare ones. *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/en/shelves/linguistics/) ## Cited by Cited by 1 paper of our library cites it (of the 140884 whose bibliographies we read); each has a page here. - [A Corpus of Persuasion Techniques in Slavic Languages](https://md.research.elamur.ai/free/en/papers/piskorski-2026-corpus-persuasion-techniques-slavic-languages/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @misc{alzahrani2024investigating, title = {{Investigating Persuasion Techniques in Arabic: An Empirical Study Leveraging Large Language Models}}, author = {Alzahrani, Abdurahmman and Babkier, Eyad and Yanbaawi, Faisal and Yanbaawi, Firas and Alhuzali, Hassan}, year = {2024}, doi = {10.48550/arxiv.2405.12884}, eprint = {2405.12884}, archivePrefix = {arXiv}, url = {https://doi.org/10.48550/arxiv.2405.12884} } ``` - [← previous Investigating Data Contamination in Modern Benchmarks for Large Language Models](https://md.research.elamur.ai/free/en/papers/deng-2024-investigating-data-contamination-modern-benchmarks/) - [next → Is Sarcasm Detection A Step-by-Step Reasoning Process in Large Language Models?](https://md.research.elamur.ai/free/en/papers/yao-2024-sarcasm-detection-step-step-reasoning/) - [↑ back to papers of 2024](https://md.research.elamur.ai/free/en/papers/2024/page/3/) ## References 44 references, 10 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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