# Enhancing Propaganda Detection in Arabic News Context Through Multi-Task Learning
###### Abstract
Although research on Arabic propaganda is still in its early stages, English propaganda detection has yielded remarkable results. Arabic is the fourth most used language on the internet, with around 237 million Arab users [13]. However, detecting propaganda in Arabic poses unique challenges owing to its complex morphology, orthographic ambiguity, limited availability of linguistic resources (e.g., lexicons), and dialectal variances [14]. Text news provides several indicators that can be used to assess its content's credibility. Previous studies on propaganda detection have primarily focused on training models solely for propaganda detection without incorporating other auxiliary tasks. Additionally, existing studies have shown a positive interaction between propaganda and sentiment, whereas others have found emotion to be efficient in propaganda detection models. Moreover, the detection and analysis of MTL for propaganda detection has only been attempted in a few studies, particularly in English [15; 16] and Arabic [17; 18; 19], without focusing on sentiment and emotion as critical related tasks. However, to the best of our knowledge, none of these studies have fully considered sentiment and emotion as integral auxiliary tasks in propaganda detection. To date, no studies have focused on Arabic propaganda detection's emotional and sentiment dimensions. Building on this, we aim to enhance the propaganda detection model by integrating knowledge of other opinion dimensions (sentiment and emotion tasks). We hypothesize that MTL combining propaganda and multiple opinion dimensions will contribute to the development of more comprehensive and accurate propaganda detection systems. This study introduces MTL models designed to jointly address three interrelated tasks: propaganda detection, sentiment analysis, and emotion detection. Furthermore, the proposed models utilize various task-weighting schemes to optimize performance, with the primary objective of enhancing propaganda detection. Previous studies have demonstrated MTL's efficacy in improving machine learning techniques' performance [11, 12, 20]. However, MTL frameworks often require more complex task management and data preprocessing than STL approaches. Additionally, while transformer-based architectures have become a cornerstone of modern NLP, their adaptation to MTL scenarios presents unique challenges that are not as prevalent in other deep learning architectures [21]. Therefore, to address these challenges, our work aims to simplify the process of developing MTL models, reduce their implementation complexity, and make the process as straightforward as building STL models. Additionally, the effects of several task-weighting schemes on model performance are investigated, providing empirical insights into their relative effectiveness. This study's main contributions are summarized as follows:  
* We introduce the first MTL models that effectively enhance Arabic propaganda detection by incorporating sentiment analysis and emotion detection tasks. * We develop two MTL models: PSMTL, which combines propaganda and sentiment tasks, and PEMTL, which integrates propaganda and emotion tasks. The main objective is to investigate how incorporating sentiment and emotion can enhance propaganda detection models' effectiveness. * We propose utilizing seven distinct task-weighting techniques and provide empirical evidence supporting their effective implementation in MTL models. * We conduct a comprehensive comparative study on the proposed method compared with state-of-the-art (SOTA) studies and recent studies utilizing STL and MTL, such as [6, 22, 23, 24, 25]. The experimental results demonstrate that the proposed MTL framework outperforms all compared methods. The remainder of this article is structured as follows: Section 2 provides a review of related SOTA studies in the field, evaluates previous research, and identifies gaps that our study seeks to fill. Section 3 details the methodology employed in our study and outlines the framework and analysis.