# Enhancing Propaganda Detection in Arabic News Context Through Multi-Task Learning
## 5 Results and Discussion
### Comparisons with Previous Studies  
To conduct a comprehensive evaluation and gain insights into the advancements of our proposed approach in comparison with those of existing research, this section compares our model's performance with the results of previous studies. Table 8 summarizes the comparative results of previous research on propaganda detection. Specifically, we selected \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Categories** & **Model Name** & **Accuracy** & **Macro F1** & **Micro F1** \\ \hline \multirow{5}{*}{Proposed Task-Weighting} & MTL-DDW & 79\% & 0.772 & 0.790 \\ \cline{2-5}  & MTL-PGRW (NormalDist) & 78\% & 0.767 & 0.784 \\ \cline{2-5}  & MTL-PGRW (BernoulliDist) & 77\% & 0.765 & 0.778 \\ \cline{2-5}  & MTL-PGRW (UniformDist) & 78\% & 0.764 & 0.782 \\ \cline{2-5}  & MTL-HW & 79\% & 0.775 & 0.791 \\ \cline{2-5}  & MTL-UW & 79\% & 0.776 & 0.790 \\ \cline{2-5}  & MTL-PASUW & 78\% & 0.765 & 0.782 \\ \hline MTL Models with best Proposed Task-Weighting & MTL-Sent-UW & 79\% & 0.778 & 0.791 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Experimental results on the proposed task-weighting. SOTA systems along with all systems that evaluated the ArPro dataset [23]. Regarding the MultiProSE dataset, no prior systems have been developed for it because it was only recently released [22]. Nevertheless, we assessed the performance of our top-performing model by comparing it with the best model proposed in the dataset paper [23] and all published studies that utilized the same dataset. All models proposed for the ArPro dataset employed STL. To the best of our knowledge, there is no prior public benchmark for propaganda detection that is suitable for MTL settings, even in English, a more widely studied language. Therefore, we also compared our model with other models that applied MTL in the NLP field using the same MultiProSE dataset and model configuration, as detailed in Section 4.1, to ensure a fair comparison [6]. The models selected for comparison were as follows:  
* AraBERT [22]: AraBERT is a single-task model that extends the pretrained BERT language model by incorporating a linear classification layer into the hidden representation of the special [CLS] token. Notably, this model follows the same approach as the backbone model, making it an appropriate point for comparison. * AraBERT with Features [25]: This single-task model extends the pretrained BERT language model by adding a linear classification layer to the hidden representation of the [CLS] token, along with several additional features. These features enhance the model's propaganda detection performance. * ML with Features [25]: This is a single-task model that integrates multiple ML classifiers, each utilizing various features for improved performance in propaganda detection. * AraBERT [23]: AraBERT is a single-task model that extends the pretrained BERT language model by appending a linear classification layer to the hidden representation of the [CLS] token. This model serves as a baseline for propaganda detection. * LlamaLens [24]: LlamaLens is a specialized multilingual LLM designed for the analysis of propaganda content in news. This single-task model utilizes multilingual capabilities to assess and detect propaganda in diverse linguistic contexts. * MARBERT [6]: MARBERT is a multi-task model that extends the pretrained BERT language model by incorporating a linear classification layer into the hidden representation of the [CLS] token. This model was specifically designed for the detection of abusive content in Arabic tweets, showcasing its versatility in MTL settings. As Table 10 shows, the proposed MTL models consistently outperformed all STL and MTL models in terms of Macro-F1 score, Micro-F1 score, and accuracy. This improvement was attributed to MTL's ability to reduce overfitting during the gradient descent, leading to more effective learning. Moreover, sentiment analysis contributed to the MTL models' enhanced performance in propaganda detection.