# Cross-Lingual Cross-Domain Transfer Learning for
## 1 Introduction  
For many years, the advent of widespread Internet connectivity and the rise of social networks have empowered people to publish and disseminate information in real time. However, despite the immense potential for social good, misinformation has become increasingly prominent in public awareness [1]. Misinformation can be strategically deployed to advance specific agendas, gaining insight from organizations that want to manipulate audiences [2]. The consequences of such misinformation are often underestimated, undermining the trustworthiness of institutions and leaders [3]. Fact-checking initiatives aim to mitigate the impact of false information [4]. By employing hands-on methods to verify the sources, fact-checkers strive to establish the accuracy and truthfulness of the content. To achieve this, they identify the origin of the information and compare it with verified facts. This verification process can be time-consuming and labor-intensive, often requiring journalists to invest significant resources. Given the enormous amount of content that circulates on social networks and the rapid rate with which it spreads, fact-checkers face a constant race to provide accurate and timely verification [5]. To support the fact-checking process, various automated techniques have been developed to detect rumors [6, 7, 8, 9, 10]. These automated systems can complement existingfact-checking efforts by highlighting potential false claims, allowing fact-checkers to focus their resources on verifying these high-priority items [11]. Many techniques for automatically detecting rumors rely on machine learning models. These models not only analyze the original content, but also consider user reactions to it [6; 7; 9], which is why they are often referred to as \"propagation-based models\" [11]. Most of these techniques focus on Twitter (now X), given its importance as a news-centric social media platform that primarily deals with political and related issues. Various studies have demonstrated a robust relation between the features associated with the spread of information and the accuracy of the information itself [12; 13; 14]. For example, [PERSON] et al. [12] noted that false content is scrutinized more and questioned than factual content on social networks. Consequently, while some techniques treat the detection of rumors as a simple source categorization problem [15], the most effective methods incorporate the social context. For example, the CSI model [7] considers the user interaction structure to better represent how a rumor propagates. Similarly, [PERSON] et al. [9; 10] use neural networks to analyze the content and conversation dynamics surrounding a likely rumor, representing these interactions as a propagation tree. Despite progress in this field, most developed models are primarily designed for English [11]. Research shows challenges to adapting these existing frameworks for languages with limited resources [16]. This difficulty comes mainly from the high costs of creating and annotating datasets suitable for each specific language [17]. To address this issue, we investigate the ability to transfer knowledge from English-based models to languages with fewer resources in these tasks. According to [PERSON] et al. [18], cross-lingual and cross-domain rumor detection is an emerging task that involves adapting features of well-resourced data to identify rumors in low-resource languages and domains. This task is challenging due to the unique characteristics of each language and domain. The rationale for tackling this challenge using propagation-based models is as follows. Although rumor detection models relying on source verification can be heavily influenced by cultural and news-related context [15], propagation-based models are expected to be more resilient in transfer learning scenarios. This is because there has been no evidence to suggest that the dynamics of rumor propagation differ between countries and cultures.