# Enhancing Propaganda Detection in Arabic News Context Through Multi-Task Learning
## 2 Related Work
### STL for Propaganda Detection  
Recently, the research community has made significant efforts to detect propaganda, a form of persuasion, in online news from various news sources and social media sites. Early approaches to propaganda detection relied heavily on feature engineering combined with classical machine learning algorithms. Studies employed Naive Bayes classifiers for probabilistic propaganda identification in news and fact-checking contexts, such as [26, 27], whereas support vector machines were implemented in systems such as [28] and dedicated propaganda detection tasks such as [27, 29]. Logistic regression demonstrated the effectiveness of fine-grained propaganda detection in news corpora [30, 31]. Furthermore, hybrid approaches incorporating decision trees, such as the HAPI system [32], were developed for propaganda identification on social media. K-nearest neighbors algorithms were applied in Arabic propaganda detection shared tasks [33]. While these conventional ML techniques achieved reasonable performance, their reliance on handcrafted linguistic and syntactic features, as well as their failure to account for words' contextual meaning, limited their adaptability across different languages and evolving propaganda techniques. Therefore, there is a critical need for more sophisticated approaches.  
With the rise of deep learning, several researchers have proposed supervised models for propaganda detection that leverage deep learning techniques to automatically learn feature representations from text, including convolutional neural networks [30; 34], long short-term memory (LSTM) [26; 35; 36], and bidirectional recurrent neural networks (Bi-LSTM) [37]. However, these models require a substantial amount of annotated data that are specifically tailored to the task at hand. Acquiring such data presents a significant challenge in real-world NLP applications owing to the inherent linguistic diversity and complexity of natural languages. Consequently, a lack of suitable annotated data can impede supervised learning's effectiveness in these contexts.  
Recently, the transfer learning field in NLP has undergone a significant transformation with the advent of pretrained language models (PLMs). This approach involves leveraging knowledge from related domains, tasks, or languages by maximizing the use of unlabeled data in either the source or the target domain. Several researchers in the field of propaganda detection have embraced transfer learning by utilizing PLMs trained on extensive unlabeled data, followed by the fine-tuning of the models for the classification task. The best systems in SemEval-2020 have leveraged pretrained transformers (BERT-based models) and ensemble methods, achieving strong results in identifying propaganda spans and categories [38]. PLMs utilized for propaganda detection include ROBERTA [39] and DeBERTa [40]. Likewise, transformer-based language models specifically trained for Arabic have proven to be highly effective, such as AraBERT [17; 18; 23; 33; 41; 42; 43; 44]. Furthermore, large language models (LLMs), including GPT-4 [45] and LlamaLens [24], have been utilized.