Dual-Aspect Active Learning with Domain-Adversarial Training for Low-Resource Misinformation Detection

The rapid spread of misinformation threatens public safety and social stability. Although deep learning-based detection methods have achieved promising results, their effectiveness heavily relies on large amounts of labeled data, limiting their applicability in low-resource scenarios. Existing approaches, such as domain adaptation and metalearning, attempt to transfer knowledge from related source domains but often fail to fully address the challenges of data scarcity and annotation costs. Moreover, traditional active learning strategies typically focus solely on textual uncertainty, overlooking domain-specific discrepancies and the critical role of affective information in misinformation content. To address these challenges, this paper proposes a dual-aspect active learning framework with domain-adversarial training (DDT), tailored for low-resource misinformation detection. The framework integrates a dual-aspect sampling strategy that jointly considers textual and affective features to select samples that are both informative (diverse from labeled data) and uncertain (near decision boundaries). Additionally, a domain-adversarial training module is employed to extract domain-invariant representations, mitigating distribution shifts between source and target domains. Experimental results on multiple benchmark datasets demonstrate that DDT consistently outperforms baseline methods in low-resource settings, enhancing the robustness and generalizability of misinformation detection models.