# Enhancing Propaganda Detection in Arabic News Context Through Multi-Task Learning
## 5 Results and Discussion
### Discussion  
This study's primary objective was to enhance propaganda detection's effectiveness in Arabic news texts through the development of advanced MTL models. Building on recent advancements in MTL and the integration of auxiliary tasks, the proposed models explored the impact of various task-weighting strategies and auxiliary tasks, particularly sentiment and emotion analyses, on model performance. The experimental results presented in Sections 5.1-5.3 demonstrate substantial advancements in propaganda detection through MTL frameworks, outperforming both existing STL models and prior MTL models. The proposed MTL models consistently outperformed the STL baselines across all metrics, that is, accuracy, Macro-F1, and Micro-F1, validating the hypothesis that auxiliary tasks, such as sentiment and emotion analyses, significantly enhance propaganda detection. Furthermore, the observed improvements in the MTL models incorporating sentiment and emotion tasks aligned with the findings presented in the related work in Section 2, according to which auxiliary tasks contribute to improved task generalization and reduced overfitting during training. Specifically, the MTL-UAW model, incorporating sentiment and emotion tasks, achieved the highest Macro-F1 score (0.763), surpassing the STL benchmark, AraBERT [(23)], by 1.7%. Consistent with prior research, incremental improvements of 1-2% in Macro-F1 and accuracy are considered significant within the context of Arabic NLP, owing to the linguistic complexity and the nuanced nature of propaganda detection. The statistically significant results presented in Table 11 (_p_ < 0.05) substantiate that these improvements are robust and not attributable to random variation. Moreover, qualitative analyses, elaborated in Section 6, indicate that the proposed multi-task learning framework more effectively captures subtle instances of propaganda, particularly in texts exhibiting neutral or mixed sentiments, where conventional baseline models frequently demonstrate \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy** & **Macro-F1** & **Micro-F1** \\ \hline STL-AraBERT [(22)] & 77\% & 0.756 & 0.769 \\ \hline STL-AraBERT with Features [(25)] & 78\% & 0.768 & 0.785 \\ \hline STL-ML with Features [(25)] & 75\% & 0.708 & 0.755 \\ \hline STL-AraBERT [(23)] & NA & 0.750 & 0.767 \\ \hline STL-LlamaLens: Specialized & NA & NA & 0.747 \\ Multilingual LLM [(24)] & & & \\ \hline MTL-MARBERT [(6)] & 76\% & 0.742 & 0.757 \\ \hline MTL-Sent-LS (ours) & 79\% & 0.778 & 0.792 \\ \hline MTL-Sent-UW (ours) & 79\% & 0.778 & 0.791 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: A comparison of the best models with SOTA models. limited performance. Additionally, the integration of auxiliary sentiment and emotion tasks contributes to the enhancement of feature representations, thereby improving model robustness and providing avenues for further advancement in detection performance. The comparative analysis of two-task models, such as propaganda and sentiment and propaganda and emotion, revealed a subtle yet informative distinction between sentiment- and emotion-oriented supervision, influencing propaganda detection's overall effectiveness. Moreover, although MTL-Sent-LS and MTL-Sent-UW outperformed MTL-Emo-UW and MTL-Emo-LS in terms of the Macro-F1 score, the differences of 0.39% and 0.65%, respectively, were marginal. This suggests that both sentiment and emotion features are equally valuable for optimizing performance, with sentiment analysis offering a slightly broader contextual advantage in terms of model generalization. However, in agreement with previous studies [5, 12], the superior performance observed when using sentiment as the sole auxiliary task can be attributed to a stronger alignment between the auxiliary and main tasks as well as a reduced likelihood of negative transfer.