In the current era of digital communication and widespread use of social media, it is crucial to develop an understanding of persuasive techniques employed in written text. This knowledge is essential for effectively discerning accurate information and making informed decisions. To address this need, this paper presents a comprehensive empirical study focused on identifying persuasive techniques in Arabic social media content. To achieve this objective, we utilize Pre-trained Language Models (PLMs) and leverage the ArAlEval dataset, which encompasses two tasks: binary classification to determine the presence or absence of persuasion techniques, and multi-label classification to identify the specific types of techniques employed in the text. Our study explores three different learning approaches by harnessing the power of PLMs: feature extraction, fine-tuning, and prompt engineering techniques. Through extensive experimentation, we find that the fine-tuning approach yields the highest results on the aforementioned dataset, achieving an f1-micro score of 0.865 and an f1-weighted score of 0.861. Furthermore, our analysis sheds light on an interesting finding. While the performance of the GPT model is relatively lower compared to the other approaches, we have observed that by employing few-shot learning techniques, we can enhance its results by up to 20\%. This offers promising directions for future research and exploration in this topic\footnote{Upon Acceptance, the source code will be released on GitHub.}.

翻译：在当前数字通信和社交媒体广泛使用的时代，理解书面文本中使用的说服技巧至关重要。这些知识对于有效辨别准确信息并做出明智决策不可或缺。为满足这一需求，本文提出了一项全面的实证研究，聚焦于识别阿拉伯语社交媒体内容中的说服技巧。为实现这一目标，我们利用预训练语言模型（PLMs）并结合ArAlEval数据集，该数据集涵盖两个任务：二元分类（判断是否存在说服技巧）和多标签分类（识别文本中使用的具体技巧类型）。本研究通过挖掘PLMs的能力，探索了三种不同的学习方法：特征提取、微调和提示工程技巧。通过大量实验，我们发现微调方法在所述数据集上取得了最佳结果，f1-micro分数达0.865，f1-weighted分数达0.861。此外，我们的分析揭示了一个有趣的发现：尽管GPT模型的性能相对低于其他方法，但通过采用少样本学习技巧，我们可将其结果提升高达20%。这为未来该主题的研究与探索提供了有前景的方向\footnote{接受发表后，源代码将在GitHub上发布。}。