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.}.

本研究针对阿拉伯社交媒体内容，利用预训练语言模型（PLMs）进行了全面的经验研究，旨在识别其中的说服技术。通过特征提取、微调和提示工程技术三种学习方法的实验比较，得出微调方法在二分类和多标签分类任务上取得了最高结果，f1-micro 分数为 0.865，f1-weighted 分数为 0.861。此外，通过使用少样本学习技术，我们观察到在性能相对较低的 GPT 模型上可以提高结果达 20％，这为未来的研究和探索方向提供了有希望的方向。