# A Cross-Lingual Media Profiling Model for Detecting Factuality and Political Bias
###### Abstract  
Media profiling offers valuable insights to enhance the objectivity and reliability of news coverage by providing comprehensive analysis, but the diversity in languages posed significant challenges to our identification of factuality and political bias of non-English sources. The limitation of existing media analysis research is its concentration on a singular high-resource language, and it hardly extends to languages beyond English. To address this, we introduce xMP, a dataset for zero-shot cross-lingual media profiling tasks. xMP's cross-lingual test set encompasses 34 non-English languages and 18 language families, extending media profiling beyond English resources and allowing us to assess cross-lingual media profiling model performance. Additionally, we propose a method, named R-KAT, to enhance the model's zero-shot cross-lingual transfer learning capability by building virtual multilingual embedding. Our experiments illustrate that our method improves the transferability of models in cross-lingual media profiling tasks. Additionally, we further discuss the performance of our method for different target languages. Our dataset and code are publicly available. media profiling; cross-lingual; factuality; political bias 2024202024149837 20242024  
languages. News is produced globally, often in languages that lack extensive digital resources, making cross-lingual media profiling both crucial and challenging. In recent years, the development of media profiling datasets such as MBFC [18; 19; 20; 21] and NELA-GT [22; 23; 24; 25] has advanced the field. These datasets typically focus on textual features [18; 19; 26; 27; 28], audience homogeneity [19; 29; 30; 31], and media outlet characteristics [32; 33; 34; 35]. Yet, they remain limited by their focus on high-resource languages, overlooking the linguistic diversity necessary for global media analysis. To meet this challenge, our proposed cross-lingual media analysis dataset and model address this gap by extending bias and factuality assessments to low-resource languages worldwide. Specifically, we present xMP, a cross-lingual evaluation dataset for media profiling. xMP covers 242 media outlets and 117.3 K articles in 34 non-English languages from 18 language families, providing comprehensive annotations for both factuality and political bias. We also explore the effects of fine-grained versus coarse-grained labeling, as well as the impact of linguistic differences on media profiling. Despite advancements in deep learning-based media profiling, the field remains constrained by a reliance on high-resource language training datasets. Zero-shot cross-lingual profiling offers a promising solution by enabling models to generalize across languages that were not part of the training data. However, significant differences among languages present ongoing challenges for cross-lingual transfer [36]. In response to these challenges, we introduce R-KAT, a cross-lingual training method that does not require parallel corpora or supervised data in low-resource languages. Our method employs multistep iterative perturbations to construct a virtual multilingual news source embedding, followed by regularization across both English and multilingual embeddings, thus improving the multilingual pretrained model's cross-lingual transferability and enhancing performance in both monolingual and cross-lingual media profiling tasks. Comprehensive experiments show that while our method outperforms other baselines, zero-shot cross-lingual media profiling tasks still present challenges, particularly with political bias predictions for certain languages. The main contributions of this paper can be summarized as follows:  
* To the best of our knowledge, we present xMP, the first evaluation dataset for media profiling across 34 target languages. It adopts the fine-grained labeling setting that has been analyzed to be more suitable for media profiling tasks.