# Cross-Lingual Cross-Domain Transfer Learning for
## 2 Related Work
### Multi-Task Learning in Rumor Detection  
Multi-task learning, first introduced by [PERSON] [56], involves cooperatively training models across multiple tasks, typically focusing on a primary task enhanced by auxiliary tasks. Incorporating auxiliary tasks can improve the performance on the main task by enabling the model to learn more _robust_ and _universal representations_[29; 57]. Although [PERSON] originally proposed _joint learning_, subsequent research introduced methods such as _alternate training_ and _cascade learning_[58]. We therefore understand multi-task learning broadly as the improvement of a primary task through the support of auxiliary tasks.  
Early multi-task approaches to rumor detection typically combined rumor identification with stance classification [30]. Note that [30] does not use bot detection as an auxiliary task. Controversial posts often elicit a range of responses, forming identifiable patterns that aid in rumor detection. [PERSON] et al. [30] proposed two GRU-based architectures: the Uniform Shared-Layer Architecture (MT-US) and the Enhanced Shared-Layer Architecture (MT-ES), the latter employing task-specific layers.  
Building on this idea, [PERSON] et al. [29] distinguished between veracity classification and rumor detection, integrating rumor detection as the main task and veracity and stance classification as auxiliary tasks. Their model featured a shared layer followed by LSTM-specific task layers, dense and softmax, and refined word embeddings with pre-trained word2 vec [59]. They also evaluated a model named NileTRG* [60], employing a linear SVM based on the characteristics of the bag of words and the position.  
Subsequent work further explored multi-task architectures. [PERSON] et al. [61] developed a model that uses a shared LSTM and task-specific layers, combining their outputs to improve accuracy prediction without requiring alternate training. Similarly, [PERSON] et al. [62] introduced \"RumorSleuth,\" which leverages a Variational Autoencoder (VAE) [63] for modeling user characteristics and fusing task outputs.  
[PERSON] et al. [24] enhanced multi-task learning by combining word2 vec and positional embeddings [20] and introducing an attention/gate mechanism to optimize cross-task learning. [PERSON] et al. [64] proposed Hierarchical-PSV, integrating Conversational-GCN for stance and Stance-Aware RNN for veracity prediction.  
More recent advances include a Coupled Transformer [65], leveraging user stance via BERT for thread-level interaction modeling. Along the same lines, [PERSON] et al. [66] introduced emotion classification as an auxiliary task, based on the theories of [PERSON] [67] and [PERSON] [68], which significantly improves disinformation detection.  
Other notable models include [PERSON] et al. [69]'s reinforcement-learning-based model for early rumor detection, [PERSON] et al. [70]'s hierarchical heterogeneous graph for cross-topic feature propagation, and [PERSON] et al. [71]'s Multi-task Multimodal Rumor Detection Framework (MMRDF), which integrates text and image classifiers.  
Expanding task diversity, [PERSON] et al. [72] developed SC-HA-MTL, combining content- and writing style-based rumor detection with hierarchical attention. Similarly, [PERSON] et al. [73] generalized deception detection in news, tweets, and reviews using shared and domain-specific branches with LSTM and BERT.  
Focusing on graph-based techniques, [PERSON] et al. [74] used sBERT and Graph Attention Networks (GAT) to capture user stance and predict the veracity of rumors, while [PERSON] et al. [75] integrated shared and task-specific graph channels for modeling structural and textual features. [PERSON] et al. [76] modeled subthread and stance-rumor interactions to improve rumor detection performance.  
Exploring transfer effects in multi-task settings, [PERSON] et al. [77] demonstrated that sentiment analysis, fake news detection, and topic detection benefited from multi-task learning, whereas stance detection faced negative transfer.