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

Luyao Hu, Guangpu Han, Shichang Liu, Yuqing Ren, Xu Wang, Zhengyi Yang () and Feng Jiang
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Luyao Hu: Chongging Division, PetroChina Southwest Oil & Gasfield Company, Chongqing 400707, China
Guangpu Han: Chongging Division, PetroChina Southwest Oil & Gasfield Company, Chongqing 400707, China
Shichang Liu: Chongging Division, PetroChina Southwest Oil & Gasfield Company, Chongqing 400707, China
Yuqing Ren: Chongging Division, PetroChina Southwest Oil & Gasfield Company, Chongqing 400707, China
Xu Wang: Chongging Division, PetroChina Southwest Oil & Gasfield Company, Chongqing 400707, China
Zhengyi Yang: School of Big Data and Software Engineering, Chongqing University, Chongqing 401331, China
Feng Jiang: School of Big Data and Software Engineering, Chongqing University, Chongqing 401331, China

Mathematics, 2025, vol. 13, issue 11, 1-22

Abstract: 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.

Keywords: low-resource misinformation detection; discrepancy aspect; uncertainty aspect; adversarial training; active learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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