# Cross-Lingual Cross-Domain Transfer Learning for
## 5 Discussion of Results
### Comparison of Results
#### 5.3.2 Stylistic Metric Comparison  
In order to present a descriptive analysis of linguistic metrics applied to true and false rumors in Spanish, using both fine-tuning and zero-shot approaches, four specific metrics are considered. Each metric is computed as follows: for every propagation tree, the metric is applied to the root message as well as to all replies within the conversation tree. Subsequently, the average value for each tree is calculated, resulting in a single representative metric per conversation or propagation tree. The metrics used are number of words, number of sentences, average token length, and perplexity. The label TP denotes a false rumor that has been correctly classified, while TN indicates a true rumor that has been correctly classified. The results for the fine-tuning setting are presented first, followed by those obtained using the zero-shot approach. For the results obtained through fine-tuning, Figure 7 presents the different metrics across all trees, grouped by classification. From the figure, the following observations can be made:  
* In terms of word count, the FN class shows substantial variability: while some texts are extremely short, others are significantly longer. This inconsistency may introduce confusion for the classifier. Additionally, the lower median word count observed in this class indicates that many instances consist of only a few words, which could further impair the accuracy of the classification. In contrast, the FP boxplot reveals that texts in this category tend to be more uniform in length. This regularity might serve as a cue that the classifier relies upon, potentially resulting in systematic misclassification. * In the case of the number of sentences per message, misclassified instances (FP and FN) tend to include fewer sentences than correctly classified ones (TP and TN). This pattern suggests that shorter texts can hinder the performance of the classifier. Similar to the previous metric, the FN group shows a more noticeable reduction in the median number of sentences--remaining generally low--with the presence of an outlier that could contribute to the classifier's confusion. * On average, false positives (FPs) exhibit a greater token length, which may adversely affect the classifier's performance. In contrast, true negatives (TNs) and false negatives (FNs) typically consist of shorter tokens, potentially reflecting simpler texts. Interestingly, true positives (TPs) include several outliers. Overall, the average token length seems to play a role in the misclassification of FPs compared to the other categories. * According to the perplexity metric, false positives (FPs) tend to have low values, indicating that the model considered these instances predictable, even though it misclassified them. In contrast, false negatives (FNs) exhibit a wider distribution of perplexity scores, ranging from low to high, which suggests that the model fails on both straightforward and more complex texts. For true negatives (TNs) and true \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Embedding** & **Macro-F1** \\ \hline BiGRU & mBERT-sum-four-last & 0.666 \\ Base0 & sBERT-default & 0.701 \\ Base0 & mBERT-sum-four-last & 0.726 \\ BiGRU & mBERT-embedding & 0.726 \\ BiGRU & sBERT-default & 0.729 \\ BiGRU & mBERT-second-to-last & 0.744 \\ Base0 & mBERT-embedding & 0.767 \\ Base0 & mBERT-second-to-last & 0.768 \\
**CLM-MTL-RD (CLM14)** & **mBERT-second-to-last** & **0.783** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of the results of the best-performing model for Spanish data with baseline models, sorted by Macro-F1. positives (TPs), correct classifications are generally associated with texts of moderate perplexity, reflecting a balanced level of complexity. For the results obtained using the zero-shot approach, as shown in Figure 8, the following observations can be made:  
* In the case of words, FP contains a larger number of words. When compared to the results of TP, this difference may contribute to classification errors.