# Enhancing Propaganda Detection in Arabic News Context Through Multi-Task Learning
## 6 Error Analysis
### Per-Class Performance  
Figures 4 and 5 show the classification report and confusion matrix for each model. In Figures 4 and 5, matrix (a) corresponds to the MTL-Sent-UW model, whereas matrix (b) presents the results for MTL-Emotion-UW. The diagonal elements of each confusion matrix indicate correct predictions, whereas the off-diagonal elements represent misclassified instances. In general, both MTL variants achieved competitive results. Matrix (a) showed that the model tended to under-predict propaganda. The comparatively thick shading in the lower-left cell (FN = 142) signaled many persuasive texts that slipped through as \"neutral.\" Furthermore, matrix (b) revealed a shift where the lower-left cell lightens, confirming the  
recall improvement, whereas the upper-right cell darkens slightly (FP = 138). Emotion cues helped the model retrieve alarmist or fear-laden propaganda that sentiment alone missed, but the same cues occasionally raised neutral crisis reports to class propaganda. We observed that the propaganda class was consistently easier to learn than nonpropaganda. However, the two models differed in how they reached that conclusion. In matrix (a), the propaganda F1-score was 0.8338, but its recall stalled at 0.8293, leaving 142 FN propaganda texts undetected. In contrast, in the emotion auxiliary task, recall climbed to 0.8425 and the F1-score to 0.8390, which was a relative gain of 0.6 with 11 fewer FNs (only 131). The price was a marginal increase of five FPs (from 133 to 138); hence, the overall accuracy rose from 79.26% to 79.71%. Overall, the MTL-Emotion-UW model achieved the strongest overall performance, as shown in Table 12, with 269 text misclassifications (138 FPs + 131 FNs). In contrast, MTL-Sent-UW attained a slightly lower accuracy of 79.26% and produced 275 misclassifications (133 FPs + 142 FNs). Notably, MTL-Emotion-UW exhibited higher TPs for propaganda and slightly lower FNs, indicating improved sensitivity to propaganda cues compared to MTL-Sent-UW. Sentiment is a useful cue for spotting propaganda. Messages that try to persuade people often carry clear feelings. Harsh attacks sound very negative, whereas patriotic  
Figure 4: Confusion matrices for (a) MTL-Sent-UW and (b) MTL_Emotion_UW models. Figure 5: Classification reports for (a) MTL-Sent-UW and (b) MTL_Emotion_UW models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **FP** & **FN** & **Total Errors** & **Accuracy** & **Recall (Class Propaganda)** \\ \hline MTL-Sent-UW & 133 & 142 & 275 & 0.7926 & 0.8293 \\ \hline MTL-Emotion-UW & 138 & 131 & 269 & 0.7971 & 0.8425 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Error counts extracted from the confusion matrices. Bold indicates the best results. slogans sound very positive. By marking sentences with strong positive or negative tones, a sentiment-aware model can focus on parts of the text where propaganda is more likely and avoid many neutral factual statements. While sentiment alone cannot find every subtle case, it offers a simple, low-cost signal that improves the early detection of persuasive content. In contrast, emotion introduces nuanced affective signals such as anger and fear that exhibit a strong association with persuasive messages. When these affective cues coincide with propaganda indicators, the model effectively identifies challenging instances, particularly neutral-toned statements that implicitly convey fear appeals or employ glittering generalities. However, this approach also leads to a modest increase in FPs, as highly emotional yet nonpropagandistic content like pandemic updates and disaster statistics tends to be occasionally misclassified as propaganda. The remaining challenge is to distinguish genuine persuasive intent from emotionally charged but factual reporting. This is a task that may require discourse-level context or a dedicated rumor detection auxiliary task in future work.