# Abusive Content Detection in Arabic Tweets Using Multi-Task Learning and Transformer-Based Models
## 5 Results and Discussion
### Results of the Proposed Framework
This is because that the selected models MARBERT, CAMeLBERT, QARiB, ArabicBERT, and AraBERTv0.2 were pretrained on DA datasets. In addition, we found that MARBERT achieved better performance compared to the others in three out of five attributes (the best results are shown in bold). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Attribute**} & \multirow{3}{*}{
\begin{tabular}{c} **Pretrained Arabic** \\ **LMs** \\ \end{tabular} } & \multicolumn{6}{c}{**Before NLPaug**} & \multicolumn{6}{c}{**After NLPaug**} \\ \cline{3-10}  & & & **Macro-Avg** & & **Micro F1** & & **Macro-Avg** & & **Micro-F1** \\ \cline{3-10}  & & **Prec.** & **Recall** & **F1** & **Acc.** & **Prec.** & **Recall** & **F1** & **Acc.** \\ \hline \multirow{6}{*}{**Directness**} & MARBERT & 0.63 & 0.60 & 0.60 & 0.64 & 0.68 & 0.65 & **0.65** & 0.67 \\ \cline{2-10}  & CAMeLBERT & 0.64 & 0.63 & 0.63 & 0.65 & 0.64 & 0.62 & 0.62 & 0.64 \\ \cline{2-10}  & QARiB & 0.70 & 0.61 & 0.58 & 0.66 & 0.65 & 0.64 & 0.64 & 0.65 \\ \cline{2-10}  & ArabicBERT & 0.64 & 0.59 & 0.57 & 0.64 & 0.63 & 0.61 & 0.60 & 0.63 \\ \cline{2-10}  & AraBERTv0.2 & 0.62 & 0.60 & 0.59 & 0.63 & 0.65 & 0.64 & 0.64 & 0.66 \\ \hline \multirow{6}{*}{**Hostility**} & MARBERT & 0.28 & 0.29 & 0.26 & 0.41 & 0.45 & 0.46 & **0.44** & 0.46 \\ \cline{2-10}  & CAMeLBERT & 0.30 & 0.31 & 0.26 & 0.43 & 0.44 & 0.44 & 0.43 & 0.45 \\ \cline{2-10}  & QARiB & 0.30 & 0.29 & 0.25 & 0.41 & 0.42 & 0.42 & 0.40 & 0.43 \\ \cline{2-10}  & ArabicBERT & 0.23 & 0.28 & 0.25 & 0.40 & 0.43 & 0.43 & 0.40 & 0.45 \\ \cline{2-10}  & AraBERTv0.2 & 0.24 & 0.27 & 0.24 & 0.38 & 0.42 & 0.43 & 0.41 & 0.44 \\ \hline \multirow{6}{*}{**Target**} & MARBERT & 0.57 & 0.61 & 0.57 & 0.63 & 0.79 & 0.80 & 0.79 & 0.79 \\ \cline{2-10}  & CAMeLBERT & 0.58 & 0.61 & 0.58 & 0.59 & 0.81 & 0.82 & **0.82** & 0.81 \\ \cline{2-10}  & QARiB & 0.58 & 0.65 & 0.60 & 0.63 & 0.78 & 0.79 & 0.78 & 0.78 \\ \cline{2-10}  & ArabicBERT & 0.58 & 0.51 & 0.53 & 0.58 & 0.78 & 0.78 & 0.77 & 0.77 \\ \cline{2-10}  & AraBERTv0.2 & 0.60 & 0.61 & 0.59 & 0.61 & 0.80 & 0.81 & 0.80 & 0.80 \\ \hline \multirow{6}{*}{**Group**} & MARBERT & 0.57 & 0.60 & 0.58 & 0.77 & 0.89 & 0.89 & **0.89** & 0.88 \\ \cline{2-10}  & CAMeLBERT & 0.76 & 0.78 & 0.76 & 0.75 & 0.84 & 0.84 & 0.84 & 0.84 \\ \cline{2-10}  & QARiB & 0.75 & 0.78 & 0.75 & 0.77 & 0.81 & 0.81 & 0.80 & 0.81 \\ \cline{2-10}  & ArabicBERT & 0.71 & 0.63 & 0.66 & 0.72 & 0.80 & 0.80 & 0.79 & 0.80 \\ \cline{2-10}  & AraBERTv0.2 & 0.72 & 0.70 & 0.71 & 0.70 & 0.81 & 0.81 & 0.80 & 0.83 \\ \hline \multirow{6}{*}{**Annotator**} & MARBERT & 0.17 & 0.17 & 0.16 & 0.36 & 0.55 & 0.54 & 0.54 & 0.55 \\ \cline{2-10}  & CAMeLBERT & 0.19 & 0.17 & 0.16 & 0.35 & 0.56 & 0.57 & **0.56** & 0.57 \\ \cline{1-1} \cline{2-10}  & QARiB & 0.14 & 0.13 & 0.13 & 0.26 & 0.15 & 0.15 & 0.28 & 0.55 \\ \cline{1-1} \cline{2-10}  & ArabicBERT & 0.14 & 0.16 & 0.14 & 0.26 & 0.46 & 0.50 & 0.46 & 0.51 \\ \cline{1-1} \cline{2-10}  & AraBERTv0.2 & 0.18 & 0.17 & 0.16 & 0.28 & 0.38 & 0.41 & 0.38 & 0.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Pretrained Arabic LM performance results.