# Enhancing Propaganda Detection in Arabic News Context Through Multi-Task Learning
## 2 Related Work
### Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Language** & **Model Name** & **MTL Used?** & **Best Score** & **Limitation** \\ \hline
[32] & English & \begin{tabular}{c} SVM, Naïve Bayes, \\ Decision Tree, \\ Logistic Regression \\ \end{tabular} & \(\times\) & \
\begin{tabular}{c} 0.647 \\ F1-Score \\ \end{tabular} &
\begin{tabular}{c} Limited by dependency on manual \\ hybrid feature engineering, \\ impacting generalization to \\ other domains. \\ \end{tabular} \\ \hline
[33] & Arabic & \begin{tabular}{c} SVM, Naïve Bayes, \\ Stochastic Gradient \\ Descent, Logistic \\ Regression, Random \\ Forests, K-nearest \\ Neighbor, AraBERT \\ \end{tabular} & \(\times\) & \
\begin{tabular}{c} 0.649 \\ F1-Score \\ \end{tabular} &
\begin{tabular}{c} Demands an extensive amount of \\ data to effectively train the \\ deep model \\ \end{tabular} \\ \hline
[34] & Urdu & \begin{tabular}{c} CNN and \\ Logistic Regression \\ \end{tabular} & \(\times\) & \
\begin{tabular}{c} 91\% \\ accuracy \\ \end{tabular} &
\begin{tabular}{c} Limited by single-task focus without \\ task interactions. \\ \end{tabular} \\ \hline
[35] & English & LSTM & \(\times\) & \
\begin{tabular}{c} 0.687 \\ F1-Score \\ \end{tabular} &
\begin{tabular}{c} Demands an extensive amount of \\ data to effectively train the \\ deep model. \\ \end{tabular} \\ \hline
[36] & English & \begin{tabular}{c} Gradient Boosting, \\ LSTM \\ \end{tabular} & \(\times\) & \
\begin{tabular}{c} 96.00\% \\ Accuracy \\ \end{tabular} &
\begin{tabular}{c} Considers only individual tasks \\ without multi-task relationships. \\ \end{tabular} \\ \hline
[37] & English & Bi-LSTM & \(\times\) & \
\begin{tabular}{c} 0.7500 \\ F1-Score \\ \end{tabular} &
\begin{tabular}{c} Limited scalability due to the \\ training complexity of Bi-LSTM, \\ especially with larger datasets. \\ \end{tabular} \\ \hline
[39] & English & ROBERTA & \(\times\) & \
\begin{tabular}{c} 88.32\% \\ Accuracy \\ \end{tabular} &
\begin{tabular}{c} Considers only individual tasks \\ without multi-task relationships. \\ \end{tabular} \\ \hline
[40] & English & \
\begin{tabular}{c} RoBERTa, \\ BERT, DeBERTa \\ \end{tabular} & \(\times\) &
\begin{tabular}{c} 0.6000 \\ F1-Score \\ \end{tabular} & \\ \hline
[41] & Arabic & \begin{tabular}{c} AraBERT, \\ MARBERT, \\ ARBERT, \\ XLMRoBERTa, \\ AraELECTRA. \\ DeHateBERT \\ \end{tabular} & \(\times\) & \
\begin{tabular}{c} 0.565 \\ F1-Score \\ \end{tabular} &
\begin{tabular}{c} Considers only individual tasks \\ without multi-task relationships. \\ \end{tabular} \\ \hline
[42] & Arabic & AraBERT & \(\times\) & \
\begin{tabular}{c} 0.5850 \\ F1-Micro \\ \end{tabular} &
\begin{tabular}{c} Relies on named entity recognition \\ and data augmentation, limiting \\ effectiveness across varying \\ propaganda techniques. \\ \end{tabular} \\ \hline
[43] & Arabic & AraBERT & \(\times\) & \
\begin{tabular}{c} 0.6020 \\ F1-Micro \\ \end{tabular} &
\begin{tabular}{c} Data augmentation may introduce \\ noise, limiting the robustness of \\ AraBERT in propaganda detection. \\ \end{tabular} \\ \hline
[44] & Arabic & AraBERT-Twitter-v2 & \(\times\) & \
\begin{tabular}{c} 0.5666 \\ F1-Micro \\ \end{tabular} &
\begin{tabular}{c} Considers only individual tasks \\ without multi-task relationships. \\ \end{tabular} \\ \hline
[45] & Arabic & GPT-4 & \(\times\) & \
\begin{tabular}{c} 0.4025 \\ F1-Micro \\ \end{tabular} &
\begin{tabular}{c} Limited by GPT-4’s sensitivity to \\ prompts, affecting performance \\ consistency across varied narratives. \\ \end{tabular} \\ \hline
[24] & Arabic & LlamaLens & \(\times\) &
\begin{tabular}{c} Performance is limited by \\ multilingual capability constraints \\ and domain-specific \\ fine-tuning requirements. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._