# Cross-Lingual Cross-Domain Transfer Learning for
## 1 Introduction
Furthermore, recent studies have shown similarities in propagation dynamics and user interactions between English [13] and Spanish language settings [14]. This research outlines the creation and empirical assessment of an innovative technique to detect rumors. Using cross-lingual cross-domain learning methods, we employ tools and methodologies to generate word embeddings, specifically those based on the transformer encoder architecture [19] used to create BERT [20]. Our primary goal is to optimally leverage the limited resources available in less commonly spoken languages while benefiting from the extensive resources in widely spoken languages. As part of our ongoing study, we focus on Spanish as the target language and English as the resource-rich language from which we draw various insights. The core concept of our proposal is to integrate resources for various tasks related to rumor detection within the framework of transfer learning. Performing in a multi-task setting, we take advantage of the availability of multiple datasets in English to facilitate the transfer of learning to Spanish. Our research demonstrates that using multiple datasets in English for complementary tasks improves the performance of the rumor detection model in the target language. Specifically, our proposal defines a multi-task setting comprising bot detection, stance classification, and rumor detection modules. As discussed later, _multi-task_ should not be confused with _joint learning_. Our work focuses on what is called _cascade-based_ learning, which is a sequential approach. To favor reproducibility, the source code for the models, training, and experimental setup is available online at [[https://github.com/eprovidel/rumor-detection](https://github.com/eprovidel/rumor-detection)]([https://github.com/eprovidel/rumor-detection](https://github.com/eprovidel/rumor-detection)) (accessed on 22 June 2025). This paper offers three contributions, as outlined below:  
* To the best of our knowledge, we are the first to integrate multi-task and cross-lingual learning in the context of rumor detection. Related work has concentrated on traditional NLP tasks [21, 22], or employed just one of these techniques, but not both [23, 24]. * To the best of our knowledge, our research is the first to investigate the role of \"bot detection\" [25, 26, 27, 28], within a multi-task architecture to improve rumor detection efforts. Previous studies have mainly focused on the use of \"stance classification\" for rumor detection [29, 30, 31]. * We introduce a methodology that effectively utilizes diverse datasets--varying in structure, language, and task--without requiring consolidation into a single, fully annotated dataset. Unlike previous work, our experimental approach leverages existing datasets in their current form. It employs a data transfer strategy from high-resource languages, particularly English, to Spanish. The organization of this paper is as follows. Section 2 reviews related work, explicitly focusing on rumor detection (Section 2.1), multi-task learning (Section 2.2), and cross-lingual learning (Section 2.3). Next, Section 3 presents the core definitions necessary for the work and describes the proposed model, starting with a general overview followed by detailed descriptions of each stage. The experimental results are presented in Section 4, which describes the datasets used, the results obtained, and the comparison with the baseline models. In Section 5, we discuss the main findings and limitations of the study, a comparison of the results considering base models, a comparison using different metrics of the text, and an ablation study. Finally, Section 6 summarizes the impact, reach, and limitations of our research and identifies venues for future research.