Structure-adaptive Adversarial Contrastive Learning for Multi-Domain Fake News Detection (2025)

Abstract: The rapid proliferation of fake news across multiple domains poses significant threats to society. Existing multi-domain detection models typically capture domain-shared semantic features to achieve generalized detection. However, they often fail to generalize well due to poor adaptability, which limits their ability to provide complementary features for detection, especially in data-constrained conditions. To address these challenges, we investigate the propagation-adaptive multi-domain fake news detection paradigm. We propose a novel framework, Structure-adaptive Adversarial Contrastive Learning (StruACL), to adaptively enable structure knowledge transfer between multiple domains. Specifically, we first contrast representations between content-only and propagation-rich data to preserve structural patterns in the shared representation space. Additionally, we design a propagation-guided adversarial training strategy to enhance the diversity of representations. Under the StruACL objective, we leverage a unified Transformer-based and graph-based model to jointly learn transferable semantic and structural features for detection across multiple domains. Experiments on seven fake news datasets demonstrate that StruACL-TGN achieves better multi-domain detection performance on general and data-constrained scenarios, showing the effectiveness and better generalization of StruACL.

## introduction nowadays, mainstream social platforms have facilitated the news dissemination in a faster and cheaper way. nevertheless, the ease has also caused the wide spread of fake news, which has had detrimental effects on individuals and society @xcite . triggered by the negative impact of fake news, fake news detection has become a pressing challenge due to its widespread impact across diverse platforms and domains. news content and its corresponding user engagements (i.e., tree-structured propagation) are two key data types in detecting fake news. contentbased detection methods @xcite @xcite capture intrinsic semantic or linguistic features of claim tweets to detect fake news. propagation-based detection methods @xcite @xcite @xcite @xcite are designed to integrate structural features to complement textual content for detection. nevertheless, in real-world scenarios, labeled data for fake news is often scarce, particularly in specific low-resource domains or emerging topics, which hinders detection performance. recently, multi-domain fake news detection has been widely studied to leverage and integrate knowledge from multi-domain data to improve target-domain detection @xcite @xcite @xcite , alleviating the data limitation challenge to some extent. however, the representations learned by most existing multi-domain detection paradigms fail to generalize well due to poor adaptability to the propagation structure. firstly, as shown in figure 1 , some multi-domain approaches primarily focus on learning domain-invariant or domain-shared semantic features @xcite on content-only training data. however, semantic features inherently differ from structural patterns, rendering these content-based methods inadequate for generalizing to samples that involve propagation. furthermore, directly extending domainspecific propagation-based methods struggles to effectively adapt to detection scenarios lacking propagation structures, resulting in suboptimal detection performance for content-only samples @xcite . therefore, a critical challenge lies in learning more robust representations by enhancing structure adaptability for multi-domain fake news detection. in this paper, we study a novel propagationadaptive multi-domain fake news detection paradigm, where the detection model is trained on both propagation-based data and content-only data. our goal is to enhance generalization for both types of input. to achieve this, we propose a new propagation structure-adaptive adversarial contrastive learning framework (struacl) to adaptively learn generalized semantic and structural representations for multi-domain fake news detection. specifically, we first design a new structure-aware contrastive learning (strucl) objective to facilitate the adaptive transfer of structure knowledge during multidomain training. with the guidance of structure label, strucl leverages contrastive learning to differentiate representations between samples with and without propagation, effectively capturing and retaining structural knowledge in the shared representation space. by integrating this structural information, the learned representations become more informative, allowing the model to achieve enhanced performance in detecting fake news across both propagation-based and content-only domains. additionally, we design a propagation-guided adversarial training (pat) strategy to enhance the diversity of representations under the dataconstrained condition. pat adaptively performs adversarial perturbations on original embeddings using the fast gradient method (fgm) @xcite to generate worst-case samples for both content-only and propagation-based inputs. by jointly contrasting on both original and adversarial samples, the model can further effectively learn fine-grained semantic and structure knowledge via retaining the propagation-adaptive feature consistency. for the model architecture, we adopt a shared transformer-based and graph-based network to jointly encode semantic and structural features from news content and available propagation across multiple domains, respectively. under the proposed objective, our struacl-tgn generalizes well across both content-only and propagationstructured domains. we conduct experiments on seven fake news datasets with and without propagation. ## methodology in this section, we first describe the problem definition of propagation-adaptive multi-domain fake news detection. then, we propose a new struacl-tgn to learn generalized representations on both domains with propagation data and content-only domains. the overall architecture is shown in ## conclusion this paper studies a propagation-adaptive multidomain fake news detection paradigm. to achieve this, we develop a novel struacl-tgn to learn generalized representations from propagationbased and content-only domains. struacl contrasts semantic and structural representations to preserve and transfer structural knowledge, while introduces propagation-guided adversarial training to enhance the diversity of representations. experiments on seven datasets show that struacl-tgn achieves superior multi-domain detection on general and data-constraint settings, proving the effectiveness and generalization of struacl.