# A Cross-Lingual Media Profiling Model for Detecting Factuality and Political Bias
## 5 Experiments
### RQ3: Can English-Trained Models Effectively Adapt to Divergent Languages? To address RQ3, we display the experimental results of our method on some languages in Table 6. \begin{table}
\begin{tabular}{l l c c c c c} \hline \hline  & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Monolingual (EN)**} & \multicolumn{3}{c}{**Cross-Lingual**} \\  & & **ACC** & **F1-Macro** & **MAE** & **ACC** & **F1-Macro** & **MAE** \\ \hline \multirow{4}{*}{**Model**} & XLM-R & 0.241 & 0.244 & 0.695 & 0.177 & 0.206 & 1.330 \\  & +ADV & 0.439 & 0.367 & 0.405 & 0.247 & 0.268 & 1.090 \\  & +RS-RP & 0.467 & 0.381 & 0.477 & 0.243 & 0.260 & 1.154 \\  & +RS-DA & 0.224 & 0.239 & 0.672 & 0.173 & 0.195 & 1.384 \\  & +R-KAT & **0.544** & **0.437** & **0.385** & **0.302** & **0.315** & **1.062** \\ \hline \multirow{4}{*}{**Model**} & XLM-R & 0.223 & 0.214 & 0.779 & 0.137 & 0.180 & 0.985 \\  & +ADV & 0.705 & 0.534 & **0.504** & 0.126 & 0.144 & 1.146 \\  & +RS-RP & 0.575 & 0.557 & 0.603 & 0.186 & 0.201 & 1.114 \\  & +RS-DA & 0.215 & 0.235 & 0.718 & 0.163 & 0.201 & **0.973** \\  & +R-KAT & **0.740** & **0.571** & 0.508 & **0.214** & **0.214** & 1.337 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The experimental results for media profiling tasks on xMP, encompassing both English and zero-shot cross-lingual media profiling, are presented. Bold text denotes the best performance, while underlined text signifies the second-best performance. \begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline  & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Monolingual (EN)**} & \multicolumn{3}{c}{**Cross-Lingual**} \\  & & **ACC** & **F1-Macro** & **MAE** & **ACC** & **F1-Macro** & **MAE** \\ \hline \multirow{4}{*}{**Model**} & mBERT & 0.267 & 0.275 & 0.603 & 0.193 & 0.216 & 1.295 \\  & +ADV & 0.451 & 0.423 & 0.508 & 0.184 & 0.210 & 1.365 \\  & +RS-RP & 0.447 & 0.381 & 0.531 & 0.247 & **0.258** & 1.174 \\  & +RS-DA & 0.316 & 0.314 & 0.645 & 0.208 & 0.228 & 1.189 \\  & +R-KAT & **0.574** & **0.497** & **0.473** & **0.267** & 0.246 & **1.063** \\ \hline \multirow{4}{*}{**Model**} & mBERT & 0.222 & 0.233 & 0.740 & 0.151 & 0.171 & 1.067 \\  & +ADV & 0.413 & 0.426 & 0.607 & 0.188 & 0.200 & **1.062** \\ \cline{1-1}  & +RS-RP & 0.568 & 0.524 & 0.592 & 0.217 & 0.230 & 1.151 \\ \cline{1-1}  & +RS-DA & **0.582** & 0.490 & 0.645 & 0.205 & 0.220 & 1.209 \\ \cline{1-1}  & +R-KAT & 0.549 & **0.535** & **0.508** & **0.238** & **0.249** & 1.070 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The effectiveness of R-KAT on mBERT. Bold text denotes the best performance, while underlined text signifies the second-best performance. Table 6 presents the experimental results across specific languages using XLM-R as the backbone, with the R-KAT training strategy applied. After incorporating R-KAT, performance improvements were observed across nearly all languages on the evaluation metrics. In the factuality assessment task, performance improved for all languages except French. On average, MAE decreased by 0.331 (indicating improved performance), while the F1 score and accuracy increased by 0.101 and 0.136, respectively. For the political bias assessment task, accuracy and F1 score improved across all languages, with average increases of 0.193 and 0.144. However, some languages showed an increase in MAE, suggesting a decline in performance. As MAE is a comprehensive measure--where lower values indicate better outcomes--it should be regarded as a primary evaluation metric. The fact that some languages experienced increases in MAE despite improvements in accuracy and F1 score highlights that fine-grained news media political bias assessment in a cross-lingual context remains a significant challenge. This suggests that improvements in accuracy and F1 score do not necessarily lead to better MAE performance. According to our experimental results, while R-KAT enhances the model's cross-lingual transfer capabilities, we observed a decline in zero-shot transfer performance for languages more similar to English, whereas performance improves for those that are more different.