# A Cross-Lingual Media Profiling Model for Detecting Factuality and Political Bias
## 2 Related Work  
In this section, we present the relevant datasets and methodologies for the media profiling tasks, along with an introduction to zero-shot cross-lingual training.  
**Media Profiling Datasets and Methods.** Pioneer studies in media profiling initially focused on simple classifiers using features crafted from articles [37; 38; 39]. A significant advancement was made by [18; 19], who achieved notable advancements with the introduction of their inaugural large-scale media profiling dataset, MBFC. This dataset surpasses previous ones by 1-2 orders of magnitude, marking significant progress in the field. MBFC is designed to encompass two critical media analysis tasks: factual assessment and political bias assessment. This dataset established a baseline by aggregating features from media sites, social platforms, Wikipedia, and news articles, enabling the assessment of the relative importance of these features in media evaluation. In contrast, [PERSON] et al. [22; 23; 24; 25]introduced a substantial media evaluation dataset with regular updates. While featuring fewer elements than MBFC2018, it specifically includes news articles and corresponding tweets, spanning an entire year's worth of news coverage. This dataset proves valuable for tasks like weakly supervised fake news detection or news political bias detection. Notably, these approaches often focus on content features, neglecting content-agnostic features. Subsequent studies introduce new features such as infrastructure characteristics [32; 33; 34; 35] and audience homogeneity [19; 40; 41].  
Recent research has concentrated on integrating audience homogeneity and graph neural networks. Ref. [29] utilized information from the Alexa rank website to model overlapping audience relationships across diverse media, a notable advancement. Similarly, refs. [42; 43] used heterogeneous graphs to depict relationships among media, users, and news articles, employing relational inference operators to uncover potential interactions. However, both approaches primarily focus on media profiling in high-resource languages and do not consider the matching order between media and articles. Our goal is to construct a dataset for evaluating cross-lingual capabilities in media profiling models by collecting multilingual media and corresponding articles.  
**Zero-Shot Cross-Lingual Training.** Pretrained multilingual models, such as XLM-RoBERTa-Large (XLM-R) [44] and mBERT [45], show potential in zero-shot cross-lingual tasks. Improving alignment across languages in a multilingual embedding space through parallel corpora has proven effective [46; 47; 48; 49]. However, this approach demands extensive parallel corpora, incurring high data acquisition costs for low-resource languages [50]. Additionally, inherent semantic differences among languages hinder perfect alignment. To enhance zero-shot cross-lingual performance, some studies explore training methods using random smoothing, data augmentation, and perturbation in the training pipeline [51; 52; 53]. Inspired by previous work [54; 55; 56; 57; 58; 59; 60; 61], we propose a training method for zero-shot cross-lingual media profiling tasks, combining virtual multilingual embedding building and language regularization strategies.