# Cross-Lingual Cross-Domain Transfer Learning for
## 5 Discussion of Results
### Comparison of Results
#### 5.3.2 Stylistic Metric Comparison
* Regarding the sentence metrics, all groups exhibit similar distributions. In the case of FP, there is a higher number of sentences, indicating greater structural complexity, which could be a potential source of error. Nevertheless, the average number of sentences per tree does not appear to be a strong discriminative feature for classification. * For the length token metric, as with the previous one, all groups exhibit similar distributions. In the case of the FN group, however, there is an outlier that corresponds to a particularly complex text. This increased complexity may explain the classification error, especially considering that such outliers are absent in the other groups. * In the case of FP, the texts exhibit higher perplexity, suggesting they are more challenging to classify. This is further evidenced by some out-of-range failures, likely due to the presence of outliers. In contrast, TP and TN texts show lower perplexity, indicating that they are easier to classify. Figure 7: Stylistic metrics for Spanish using fine-tuning. In the case of words and perplexity, the differences are statistically significant (according to the Kruskal–Wallis test, with \(p\)-value < 0.05). However, this is not the case for sentences and token length, where the differences are not statistically significant. As previously noted, there are instances in which the model produces misclassifications. The following analysis provides a descriptive overview of such cases, specifically those where the zero-shot model yields an incorrect prediction, but the fine-tuned model successfully corrects it. In particular, Case 1--TP/FN--refers to situations where the zero-shot model produces a false negative (FN), while the fine-tuned model correctly identifies it as a true positive (TP). Conversely, Case 2--TN/FP--corresponds to cases where the zero-shot model yields a false positive (FP), and the fine-tuned model accurately classifies them as true negatives (TNs). In Figure 9, the following observations can be made:  
* In terms of word count, messages that were misclassified as false negatives (FNs) are significantly longer than those misclassified as false positives (FPs). This indicates that texts correctly classified as true positives (TPs) tend to be longer. These findings suggest that the zero-shot classifier struggles to handle longer texts effectively. * Regarding sentences, the situation is comparable: texts containing a greater number of sentences tended to be classified as false negatives (FNs), which in this context, correspond to true positives (TPs). This suggests that false rumors, in general, exhibit a higher structural complexity than true ones. Once again, the zero-shot classifier failed to account for this aspect. * In the case of token length, the misclassified texts tended to contain, on average, longer tokens. This suggests that the zero-shot classifier may have mistaken lexical complexity for factual accuracy. * On the perplexity metric, misclassified false negatives (FNs) exhibit significantly higher values than misclassified false positives (FPs). The zero-shot model appears overly confident in labeling simple texts as true rumors, while it struggles with more complex texts, often misclassifying them as false rumors. Figure 8: Stylistic metrics for Spanish using zero-shot methods. For all four metrics, there is insufficient evidence to support statistical significance, as determined by the Kruskal–Wallis test (\(p\)-value \(<\) 0.05). In summary, while the zero-shot model may rely primarily on surface-level features--such as text length or linguistic complexity--for classification, the fine-tuned model has learned to identify deeper semantic and structural patterns within the texts, allowing it to effectively distinguish between true and false rumors.