# Abusive Content Detection in Arabic Tweets Using Multi-Task Learning and Transformer-Based Models
## 5 Results and Discussion
### Results of the Proposed Framework  
Regardless of all the above challenges for automatic abusive content detection in Arabic, the proposed framework shows enhanced performance for the multi-aspect annotated dataset compared to the previous study [9]. The achieved results for ML, DL, and pretrained LMs are presented in Tables 4-6, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Attribute**} & \multirow{2}{*}{
\begin{tabular}{c} **ML Baseline** \\ **Models** \\ \end{tabular} } & \multicolumn{4}{c}{**Performance Metrics**} \\ \cline{3-6}  & & \multicolumn{3}{c}{**Macro-Avg**} & \multicolumn{2}{c}{**Micro-F1**} \\ \cline{3-6}  & & **Prec.** & **Recall** & **F1** & **Acc.** \\ \hline \multirow{3}{*}{**Directness**} & SVM & 0.58 & 0.57 & **0.56** & 0.59 \\ \cline{2-6}  & LR * & - & - & 0.53 & 0.56 \\ \cline{2-6}  & NB & 0.53 & 0.53 & 0.51 & 0.51 \\ \hline \multirow{3}{*}{**Hostility**} & SVM & 0.34 & 0.25 & 0.25 & 0.43 \\ \cline{2-6}  & LR * & - & - & 0.25 & 0.48 \\ \cline{2-6}  & NB & 0.26 & 0.36 & **0.30** & 0.39 \\ \hline \multirow{3}{*}{**Target**} & SVM & 0.43 & 0.40 & 0.41 & 0.59 \\ \cline{2-6}  & LR * & - & - & **0.47** & 0.53 \\ \cline{2-6}  & NB & 0.38 & 0.39 & 0.38 & 0.46 \\ \hline \multirow{3}{*}{**Group**} & SVM & 0.70 & 0.72 & **0.70** & 0.68 \\ \cline{2-6}  & LR * & - & - & 0.40 & 0.62 \\ \cline{2-6}  & NB & 0.50 & 0.43 & 0.45 & 0.52 \\ \hline \multirow{3}{*}{**Annotator**} & SVM & 0.12 & 0.15 & 0.13 & 0.39 \\ \cline{2-6}  & LR * & - & - & 0.14 & 0.46 \\ \cline{2-6}  & NB & 0.20 & 0.22 & **0.21** & 0.31 \\ \hline \hline \end{tabular}  
* Result obtained by [PERSON] et al. [9].  
\end{table} 
Table 4: ML baseline performance. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Attribute**} & \multirow{2}{*}{
\begin{tabular}{c} **DL** \\ **Models** \\ \end{tabular} } & \multicolumn{4}{c}{**Performance Metrics**} \\ \cline{3-6}  & & \multicolumn{3}{c}{**Macro-Avg**} & \multicolumn{2}{c}{**Micro-F1**} \\ \cline{3-6}  & & **Prec.** & **Recall** & **F1** & **Acc.** \\ \hline \multirow{3}{*}{**Directness**} & CNN & 0.62 & 0.56 & 0.53 & 0.61 \\ \cline{2-6}  & LSTM & 0.62 & 0.57 & 0.55 & 0.62 \\ \cline{2-6}  & BiLSTM * & - & - & **0.84** & 0.72 \\ \hline \multirow{3}{*}{**Hostility**} & CNN & 0.22 & 0.22 & 0.21 & 0.31 \\ \cline{2-6}  & LSTM & 0.21 & 0.24 & 0.22 & 0.35 \\ \cline{2-6}  & BiLSTM * & - & - & **0.31** & 0.47 \\ \hline \multirow{3}{*}{**Target**} & CNN & 0.38 & 0.34 & 0.34 & 0.55 \\ \cline{2-6}  & LSTM & 0.39 & 0.40 & 0.40 & 0.55 \\ \cline{2-6}  & BiLSTM * & - & - & **0.63** & 0.50 \\ \hline \multirow{3}{*}{**Group**} & CNN & 0.69 & 0.49 & **0.52** & 0.60 \\ \cline{2-6}  & LSTM & 0.45 & 0.46 & 0.45 & 0.61 \\ \cline{2-6}  & BiLSTM * & - & - & 0.04 & 0.58 \\ \hline \multirow{3}{*}{**Annotator**} & CNN & 0.14 & 0.16 & **0.13** & 0.42 \\ \cline{2-6}  & LSTM & 0.10 & 0.15 & 0.09 & 0.41 \\ \cline{2-6}  & BiLSTM * & - & - & 0.12 & 0.48 \\ \hline \hline \end{tabular}  
* Result obtained by [PERSON] et al. [9].  
\end{table} 
Table 5: DL performance results. As outlined in Table 4, the ML model's performance using TF-IDF improved the macro-F1 results in four out of five attributes compared to the obtained result [9]. Table 5 shows that the DL models do not show better performance in three out of five attributes; this is due to the fact that DL models require a higher amount of data, and the imbalanced dataset also affects the F1 macro scores. Therefore, this led to a limitation in our study which can be addressed in future studies by using different methods to enhance the performance. Nevertheless, pretrained Arabic LMs provide better performance compared to the other models, especially when we increase the sample size of tweets by using NLPaug library. The effect of before and after using NLPaug with pretrained LMs is presented in Table 6. The obtained results demonstrated that the use of pretrained Arabic LMs with NLPaug achieved the highest performance among all the previous obtained results.