# Cross-Lingual Cross-Domain Transfer Learning for
## 5 Discussion of Results  
### Main Findings of the Study  
This study thoroughly analyzes various transfer learning strategies from English to Spanish for rumor detection. Specifically, we have shown that the use of multilingual  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**S.** & **CLM** & **First Model** & **Second Model** & **Third Model** & **Test (\%)** & **Macro-F1** & **F1 True** & **F1 False** \\ \hline \multirow{4}{*}{12} & \multirow{4}{*}{CLM2} & & & 15 & 0.575 & 0.563 & 0.588 \\  & & & & 30 & 0.564 & 0.517 & 0.611 \\  & & & & 45 & 0.538 & 0.476 & 0.600 \\  & & & & 50 & 0.536 & 0.484 & 0.588 \\  & & & & 100 & 0.508 & 0.420 & 0.595 \\ \hline \multirow{4}{*}{30} & \multirow{4}{*}{CLM5} & \multirow{4}{*}{BotES} & \multirow{4}{*}{BotEN} & \multirow{4}{*}{–} & 15 & 0.511 & 0.467 & 0.556 \\  & & & & 30 & 0.551 & 0.462 & 0.641 \\  & & & & 45 & 0.515 & 0.416 & 0.615 \\ \hline \multirow{4}{*}{45} & \multirow{4}{*}{CLM8} & \multirow{4}{*}{Stance EN} & \multirow{4}{*}{Bot EN} & \multirow{4}{*}{–} & 15 & 0.575 & 0.563 & 0.588 \\  & & & & 30 & 0.598 & 0.567 & 0.629 \\  & & & & 45 & 0.587 & 0.565 & 0.608 \\  & & & & 50 & 0.579 & 0.571 & 0.587 \\  & & & & 100 & 0.555 & 0.532 & 0.578 \\ \hline \multirow{4}{*}{62} & \multirow{4}{*}{CLM12} & \multirow{4}{*}{**Bot EN**} & \multirow{4}{*}{**Stance EN**} & \multirow{4}{*}{**Bot ES**} & 15 & 0.566 & 0.698 & 0.435 \\  & & & & 30 & 0.624 & 0.676 & 0.571 \\ \cline{1-1}  & & & **45** & **0.667** & **0.698** & **0.636** \\ \cline{1-1}  & & & & 50 & 0.661 & 0.690 & 0.633 \\ \cline{1-1}  & & & & 100 & 0.587 & 0.611 & 0.564 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Best configurations for each data partition, using zero-shot strategy, for Spanish language.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Set.** & **CLM** & **First Model** & \begin{tabular}{c} **Second** \\ **Model** \\ \end{tabular} & \
\begin{tabular}{c} **Third** \\ **Model** \\ \end{tabular} &
\begin{tabular}{c} **Macro-F1** \\ **Model** \\ \end{tabular} \\ \hline
**73** & **CLM14** & **Stance EN** & **Bot EN** & **Bot ES** & **0.783** \\
6 & CLM1 & Bot EN & — & — & 0.768 \\
3 & Base0 & — & — & 0.768 \\ \hline \hline \end{tabular}
\end{table}
Table 7: CLMs that obtained the best results in rumor detection in Spanish using fine-tuning.  
embeddings, such as mBERT, enables the transfer of learning from English to Spanish. The results obtained from a baseline without auxiliary tasks are competitive, indicating that rumor detection, both in English and Spanish, based on the datasets used in this study, has high predictive capacity. In addition to this finding, we have shown that the use of transfer learning, specifically through CLM, is beneficial for improving the results of a strong baseline. In the most complex case analyzed in this study, rumor detection in Spanish, improvements are achieved through a CLM of arity 3.  
In general, using bot detection as an auxiliary task is relevant for rumor detection in English and when using a zero-shot strategy in the Spanish language test, serving in both cases as the initial model. In the case of models fine-tuned for rumor detection in Spanish, stance classification is prioritized as the first task. For all three cases, the results for rumor detection are promising, supported by auxiliary tasks such as bot detection and stance classification. Finally, we highlight that most of the results, that is, the 55 models fine-tuned for English and 54 for Spanish with the fine-tuned models, reach metric values higher than 0.6, which can be attributed to the transfer learning applied to the tasks: Bot EN, Bot ES, and Stance EN.