# Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features
## 4 Analysis and Discussions
[PERSON] et al. [25] used only Bag-of-Words and POS-tag \(n\)-grams, which differs from the approach of language-independent features we proposed. However, our results using stylometric features achieved better performance in predicting news classes in comparison to both works, indicating that the language-independent approach we presented may lead to better modelling of the problem of fake news detection. To understand the importance of language-independent features used, FST was conducted to compare the feature sets for all languages: \(Complexity\), \(Stylometric\), and \(Psychological\). Each combination within the three feature sets was analyzed, totaling 6 possible combinations for each of the 3 languages. The best performing algorithm from the former analysis (RF) was used to induct the models, ranking the performance by class using the \(F1\)-score metric. The results presented in Figure 6 shows that the combination \(Complexity+Stylometric\) features had the best performance, followed closely by the combination of all features. Next, without statistical difference from the first two, is the set of \(Stylometric+Psychological\) features, which had no statistical difference from using only \(Stylometric\) features. The \(Complexity\) set and the \(Complexity+Psychological\) combination come next, with a statistically significant difference from the others. The \(Psychological\) feature set was the least performing one with statistical difference from all other sets. Figure 5: Normalized confusion matrices comparing the true label of samples with the predicted label along the cross-validation process. The matrices indicates the class that was most wrongly predicted by models and the class which models was mistakenly towards to predict. Figure 6: Nemenyi post hoc test for all feature category combinations. While the \(Complexity+Stylometric\) and \(Complexity+Stylometric+Psychological\) are tied in the first place, with no significant difference because they are within the Critical Difference, it is possible to discard the \(Psychological\) set in this situation, considering that removing features makes the problem modelling less complex. The \(Complexity\) set demonstrated a good predictive power when combined with \(Stylometric\), but using the set alone or with \(Psychological\) does not improve the performance of the model significantly. Going even further, considering just the \(Stylometric\) set of features is not enough to solve the problem, using only 16 of the 21 features the model can still perform the same as \(Stylometric+Psychological\), which do not differ significantly from \(Complexity+Stylometric\) and \(Complexity+Stylometric+Psychological\). The \(Psychological\) feature demonstrated a low predictive contribution, probably because we used only one feature of this category in this work. Different than [PERSON] and [PERSON] [22], which used dictionary features in this category (e.g., the number of analytic words) we focused on features that could be assessed by all languages evaluated, thus, considering that some languages have fewer resources than others [56], we used the polarity feature which is widely available [40]. Table 4 exposes the average numerical value of each feature for each class grouped by corpus. Therefore, by analyzing the central tendency for each class through all corpora, we can discuss patterns that are present across languages, e.g., if some feature happens to have a higher value for legitimate, followed by fake and satirical for all corpora. A similar discussion is also made in [PERSON] and [PERSON] [22] and [PERSON] et al. [29]. The following features present the same pattern on all three corpus: ratio_ADP, ratio_DET and upper_case. These features have the same order of average value by class regardless of the language being evaluated. That is, the class with the highest value for a feature, the second and the last one are the same, maintaining the order, on all corpus in this work.