# Enhancing Propaganda Detection in Arabic News Context Through Multi-Task Learning
## 7 Conclusions and Future Work  
This study contributes to advancing Arabic propaganda detection by developing the first MTL models that integrate sentiment and emotion tasks with the primary task of propaganda detection. By leveraging recent advancements in MTL and auxiliary task integration, the proposed models explore various task-weighting schemes to optimize performance. The extensive experimental results presented in Section 5 demonstrate the effectiveness of the proposed MTL models in improving propaganda detection, showing significant enhancements over both STL models and prior MTL approaches. These findings emphasize the critical role of task-weighting in enhancing MTL performance for propaganda detection. This study makes an important contribution to MTL propaganda detection, particularly in highlighting the importance of task prioritization when integrating multiple dimensions of analysis. Among the seven proposed loss-weighting schemes, homoscedastic UW (MTL-UW) consistently delivers the most significant improvements, marginally outperforming HW (MTL-HW) and dynamic-difficulty weighting (MTL-DDW), and decisively surpassing PGRW (MTL-PGRW). These results emphasize the advantages of principled data-driven weighting strategies over heuristic or stochastic methods, especially when working with noisy or weakly supervised auxiliary labels. Exploration of the interplay between sentiment and emotion addresses a notable research gap, offering valuable insights into how these auxiliary tasks can enhance the performance of propaganda detection systems. Additionally, the findings underscore the benefits of incorporating auxiliary tasks to address data scarcity, thereby improving the model's generalization and robustness. Although this study offers valuable contributions, several limitations should be acknowledged. First, handling a multi-aspect annotation dataset introduces considerable complexity, often resulting in noisy or inconsistent labels that may affect model performance. Second, the scope of this study is limited to the Arabic language. Given that propaganda techniques and linguistic patterns can vary significantly across contexts, the observed performance improvements may not generalize well to other domains (e.g., news articles versus social media) or language varieties (e.g., Modern Standard Arabic versus dialectal Arabic). Third, the computational demands and increased model complexity may limit the practicality of the proposed approach in real-world, resource-constrained environments. Fourth, the multi-head MTL architecture demands elevated GPU time and memory allocation, which may hinder reproducibility for researchers working with limited hardware resources. Finally, while the task-weighting strategies--particularly homoscedastic UW--yield notable improvements, selecting optimal hyperparameters remains a challenging and time-consuming process. In future work, the model can be extended by incorporating LLM, such as GPT-4 or LLaMA, with prompt-based or in-context learning techniques. In addition, deeper linguistic cues such as argumentation structures can be explored. Developing dialect-specific resources and evaluating the generalizability across different Arabic variants is another promising direction. Finally, applying this methodology in cross-lingual and multimodal settings, such as meme or video propaganda detection, would further extend the scope and utility of this study. [PERSON] conceived and designed the analysis, developed the code, conducted the data analysis, and wrote the paper. [PERSON] and [PERSON] supervised the project and contributed to the review and editing of the paper. All authors have read and agreed to the published version of the manuscript. This This research was funded by the Deanship of Scientific Research, King Saud University. **Institutional Review Board Statement:** Not applicable. Informed Consent Statement: Not applicable. **Data Availability Statement:** This study analyzes existing datasets, which have been appropriately cited in the manuscript.