# An Explainable Artificial Intelligence Model for Detecting Xenophobic Tweets
## 6 Experimental Results and Discussion
### Classification Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{4}{c}{**Experts Xenophobia Database**} & \multicolumn{4}{c}{**Pitropakis Xenophobia Database**} \\ \hline \multicolumn{1}{c}{\multirow{2}{*}{**COMBINATION**}} & \multicolumn{2}{c}{**AVG**} & \multicolumn{2}{c}{**STD**} & \multicolumn{2}{c}{**COMBINATION**} & \multicolumn{2}{c}{**AVG**} & \multicolumn{2}{c}{**STD**} \\  & **AUC** & **F1** & **AUC** & **F1** & **AUC** & **F1** & **AUC** & **F1** & **AUC** & **F1** \\ \hline INTER+P4C & 0.864 & 0.768 & 0.084 & 0.134 & INTER+P4C & 0.794 & 0.734 & 0.137 & 0.172 \\ INTER+C4S & 0.811 & 0.718 & 0.056 & 0.084 & INTER+C4S & 0.725 & 0.635 & 0.105 & 0.143 \\ INTER+RUS & 0.781 & 0.568 & 0.015 & 0.043 & INTER+RUS & 0.657 & 0.591 & 0.025 & 0.036 \\ INTER+UND & 0.774 & 0.530 & 0.038 & 0.061 & INTER+UND & 0.656 & 0.598 & 0.026 & 0.019 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of the interpretable combinations in EXP and PXD. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{10}{c}{**(a) Classification results for the Experts Xenophobia Database**} \\ \hline \multicolumn{1}{c}{\multirow{2}{*}{**COMBINATION**}} & \multicolumn{2}{c}{**Partition 1**} & \multicolumn{2}{c}{**Partition 2**} & \multicolumn{2}{c}{**Partition 3**} & \multicolumn{2}{c}{**Partition 4**} & \multicolumn{2}{c}{**Partition 5**} & \multicolumn{2}{c}{**AVG**} & \multicolumn{2}{c}{**STD**} \\  & **AUC** & **F1** & **AUC** & **F1** & **AUC** & **F1** & **AUC** & **F1** & **AUC** & **F1** & **AUC** & **F1** \\ \hline BOW+C45 & 0.856 & 0.791 & 0.847 & 0.790 & 0.820 & 0.759 & 0.844 & 0.797 & 0.826 & 0.772 & 0.839 & 0.782 & 0.0  
When we analyze the results obtained on PXD (see Table 11b), we can observe a behavior very similar to that described above for the INTER+P4C combination. The main feature that PXD share with EXD is that the first two partitions have the best AUC and F1 scores. However, in the following partitions, a lower position is observed in the position obtained by INTER+P4C. This agrees with the STD shown in Table 9. Considering the rank distribution obtained by INTER+P4C in both AUC and F1 score metrics, it can be concluded that EXD Table 11a, has more consistent results than those obtained in PXD (see Table 11b). Finally, Figure 7 seeks to synthesize the results presented in this section. We can easily see that each point represents a combination embedding method (color) jointly with a classifier (shape) with these graphs. It obtains the best results for the AUC and F1 scores. It is important to note that each subgraph in Figure 7 shows the embedding method jointly with classifier results regarding AUC and F1 score metrics for EDX and PDX databases. We also see that each subgraph, in turn, contains two graphs. On the one hand, in Figure 7a the left section, each embedding method jointly with a classifier combination is ordered according to the AVG result obtained for the AUC and F1 score. On the other hand, in Figure 7a the right section, each combination is ordered by the median. Additionally, we can see that each subgraph has the same scale for both the F1 score and AUC score; with this, we can see that the closer the points are to each other, the embedding method jointly with a classifier combination is more robust. For example, in Figure 7a, the green circle represents TFIDF+C45. Both in the graphs, orders by AVG and by median are in a very similar position. This means that TFIDF+C45 is a very reliable combination, offering promising results for both metrics.