# A Cross-Lingual Media Profiling Model for Detecting Factuality and Political Bias
## 3 Dataset
### Data Analysis  
The xMP dataset boosts a media source count that is at least 2.3 times larger than its predecessor MBFC, accompanied by a substantial volume of articles. Although it does not provide a complete year of report coverage akin to NELA-GT, ongoing updates are planned until reaching that level. Notably, xMP surpasses other datasets with its inclusion of 35 languages, as highlighted in Table 2.  
Figure 1 shows the number of media outlets and articles by language in xMP, and we have 2620 media outlets and 44,925 articles of English data, a much larger number than in other languages. In the cross-lingual test dataset, French has the highest number of news media with 29, Spanish has the highest number of articles with 14,593, some languages such as Norwegian and Vietnamese have the lowest number of news media with only 2, and Macedonian has the lowest number of articles, with only 12. Moreover, the number of media outlets is often not proportional to the number of articles. For example, Persian only has 8 media outlets, but its number of articles is more than that of French, which has the largest number of media outlets, with 13,677 articles. This happens because some languages have fewer resources and limited media reporting on the web, resulting in uneven output rates. It proves that xMP is realistic and that data in some languages are difficult to access, so cross-lingual tasks are appropriate and necessary, and we hope to access more data in our future work. Appendix B shows the languages and corresponding language families. The label distribution of xMP is shown in Figure 2, where we plot heat maps of the label distribution of the multilingual data and the English data under two label settings. In Figure 2a,b, the distributions of English data and multilingual data under fine-grained labels are very similar by color and trend. In Figure 2c,d, the distributions differ between \begin{table}
\begin{tabular}{c c c c c} \hline
**Dataset** & **Factuality** & **Political Bias** & **Media** & **Article** & **Languages** \\ \hline MBFC-2018 & 3-point scale from MBFC & 7-point scale from MBFC & 1066 & 94.2 K & 1 \\ MBFC-2020 & 3-point scale from MBFC & 3-point scale from MBFC & 865 & 68.2 K & 1 \\ NELA-GT-2018 & Multipoint scale from 8 different & Multipoint scale from 8 different & 194 & 713 K & 1 \\  & assessment sites & assessment sites & 260 & 1.12 M & 1 \\ NELA-GT-2019 & Multipoint scale from 7 different & & & \\  & assessment sites & 260 & 1.12 M & 1 \\ NELA-GT-2020 & 6-point scale from MBFC & 10-point scale from MBFC & 519 & 1.78 M & 1 \\ NELA-GT-2021 & 6-point scale from MBFC & 10-point scale from MBFC & 367 & 1.85 M & 1 \\ NELA-GT-2022 & 6-point scale from MBFC & 10-point scale from MBFC & 361 & 1.77 M & 1 \\ Check That! 2023 Task3 & – & 3-point scale from MBFC & 1023 & 8.7 K & 1 \\ Check That! 2023 Task4 & 3-point scale from MBFC & – & 1189 & 10 K & 1 \\ xMP & 6-point scale from MBFC & 7-point scale from MBFC & 2862 & 460 K & 35 \\ \hline \end{tabular}
\end{table} 
Table 2: Summary of datasets of media profiling. English data and multilingual data under the coarse-grained label setting, especially on the label \"Extreme Left\" of political bias. English data and multilingual data are distributed more closely under fine-grained labels, which is conducive to cross-lingual transfer tasks. Media with a higher degree of extreme political bias are less factual, while media with a more neutral political stance are more factual and, therefore, more trustworthy. This correlation is also consistent with [PERSON] et al. 's conclusion [27], which is in line with our expectations and reality. The specific label distribution is shown in Appendix A. There are more media outlets with factual labels of Mixed and High, and more media with political bias labels of Least-Biased, Left-Center, and Right-Center in xMP. Although there are far more English resources than multilingual resources, it is not enough to conduct media profiling only for English media.