# Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features
## 4 Analysis and Discussions  
The first analysis was conducted to evaluate our hypothesis that language-independent features could be used to identify fake, satirical and legitimate news. With this goal, we applied the Principal Component Analysis (PCA) [54] dimensionality reduction technique for visualization purposes. This way, we can evaluate how well the proposed features model each class behavior. Figure 2 illustrates news articles distribution over a two-dimensional feature space, projected by components 1 and 2 from PCA. The projection exposes a particular behavior onto all languages, where Principal Component 1 (more than 20.00% variance explained) reflected a tendency of separation among the classes, as expected. The clearest separation between classes is observed in PT news articles, as seen in Figure 2b. On the other hand, EN news distribution has more overlaps (Figure 2a), followed by Spanish news (Figure 2c), i.e., the separation between classes is less straight forward. For all three languages, the legitimate class is positioned on the positive side of the X-axis, indicating that there is similarity in classes distribution over different languages. Fake and satirical classes were positioned on the left of the chart in all plots, with satirical samples being less scattered than fake ones. Although fake and satirical classes appear to have a more difficult distinction, it is remarkable that their position on the feature space is similar across sets. This analysis confirms the hypothesis that our language-independent features can model news behavior from different languages. PCA results corroborated with our conjecture towards the use of ML models to detect news intent between fake, satirical and legitimate classes. The models' accuracies are plotted through boxplots on Figure 3, showing the distribution of results with the central tendency being the median. The results show that \(k\)-NN was the worst algorithm regarding predictive power, with a mean accuracy of 75% (EN), 89% (PT), and 75% (ES). The ensemble algorithms, RF and XGB, achieved similar and stable results, with RF reaching 79.9% (EN), 93.9% (PT), and 82% (ES); and XGB achieving 80.3% (EN), 94.7% (PT), and 82% (ES). SVM had the second worst predictive performance on EN (79%), the best performance on PT (95%) while tying with ensemble algorithms on ES (82%). Results indicate that PT and ES collections have more linear separability comparing to EN, with a slightly more linear behavior on PT. The good predictive performance achieved by all algorithms demonstrates that the language-independent features and pipeline proposed in this study are efficient to identify fake, legitimate, and satirical news. Since the three classes are balanced on all datasets, the baseline classification accuracy is 33%. Therefore, when using the set of features proposed in this paper, the average accuracy of models reaches 84%, 2.5 times better than the baseline. To compare the models induced by the algorithms and evaluate which algorithm generated the best models, we employed the Friedman Statistical Test (FST). FST is a statistical test that ranks multiple methods over several datasets, as described in [PERSON] [55]. In FST, the null hypothesis to be tested is that all algorithms performed equally well, i.e., whether there is a significant difference among the results. If the null-hypothesis is rejected, the Nemenyi post-hoc test is used to compare the classifiers, and their performances are considered significantly different if the corresponding average ranks differ by a Critical Difference (CD) metric. For this study we use a confidence level of \(\alpha=0.05\)  
Figure 3: Results of cross-validation for each algorithm (\(k\)-Nearest Neighbors (\(k\)-NN), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB)) showing accuracy distribution for all three datasets (English, Portuguese, and Spanish).