# Enhancing Propaganda Detection in Arabic News Context Through Multi-Task Learning
###### Abstract  
Social media has become a platform for the rapid spread of persuasion techniques that can negatively affect individuals and society. Propaganda detection, a crucial task in natural language processing, aims to identify manipulative content in texts, particularly in news media, by assessing propagandistic intent. Although extensively studied in English, Arabic propaganda detection remains challenging because of the language's morphological complexity and limited resources. Furthermore, most research has treated propaganda detection as an isolated task, neglecting the influence of sentiments and emotions. The current study addresses this gap by introducing the first multi-task learning (MTL) models for Arabic propaganda detection, integrating sentiment analysis and emotion detection as auxiliary tasks. Three MTL models are introduced: (1) MTL combining all tasks, (2) PSMTL (propaganda and sentiment), and (3) PEMTL (propaganda and emotion) based on transformer architectures. Additionally, seven task-weighting schemes are proposed and evaluated. Experiments demonstrated the superiority of our framework over state-of-the-art methods, achieving a Macro-F1 score of 0.778 and 79% accuracy. The results highlight the importance of integrating sentiment and emotion for enhanced propaganda detection; demonstrate that MTL improves model performance; and provide valuable insights into the interaction among sentiment, emotion, and propaganda. proproganada detection; multi-task learning (MTL); natural language processing (NLP); sentiment analysis; emotion detection 222  
vast text streams is essential. Propaganda detection, a pivotal task in natural language processing (NLP), focuses on understanding how information is presented and interpreted, particularly within news media, and involves predicting whether a given text constitutes propagandistic content intended to influence public opinion. Multi-task learning (MTL) is a form of transfer learning in which jointly learning related tasks might result in a shared representation. Knowledge acquired from one related task can then be leveraged to improve performance in a different task. MTL has contributed to progress in a wide range of applications, especially in the era of deep learning, such as speech recognition, computer vision [3], and more recently, NLP [4]. In the NLP field, MTL can jointly handle related problems, working toward a more comprehensive understanding of language [5]. Further, MTL has proven effective in a wide range of NLP tasks, including abusive content detection, sentiment analysis, and sarcasm detection [6; 7; 8]. In general, MTL is applicable when optimization involves more than one loss function, which leads to more effective results than single-task learning (STL). When learning features is challenged in the primary task, the appropriate solution is to predict these features as an auxiliary task. Overall, there are challenges in designing an effective MTL model, including choosing appropriate related tasks, balancing their impact, and determining the shared representation level [9]. MTL has several significant advantages in the NLP field. One of its main benefits is its ability to enhance task performance through shared information use. For instance, when a model is trained on tasks such as machine translation, part-of-speech tagging, and named entity recognition, it can leverage shared representations from their training to boost performance on all tasks [10]. Another major advantage of MTL is the reduction in the amount of labelled data required for training [11]. By utilizing labelled data from one task, the model can enhance its performance on other tasks. This is particularly useful in NLP, where acquiring labelled data is often costly and time-consuming. Furthermore, MTL strengthens model generalization and robustness to previously unseen data by applying the same feature representations across multiple tasks [12].