Investigating Persuasion Techniques in Arabic: An Empirical Study Leveraging Large Language Models

TL;DR

This paper empirically investigates persuasive techniques in Arabic social media texts using large language models, comparing approaches like fine-tuning and prompt engineering, and highlights the potential of few-shot learning to improve GPT performance.

Contribution

It introduces a comprehensive study on identifying persuasive techniques in Arabic using PLMs, and demonstrates the effectiveness of fine-tuning and few-shot learning methods.

Findings

Fine-tuning achieves the highest classification scores (F1-micro 0.865).

Few-shot learning improves GPT results by up to 20%.

The study provides insights for future research in Arabic persuasive text analysis.

Abstract

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.}.