Context-Aware Political Misinformation Detection Across Claims and News Headlines: Multi-Seed Evaluation, Cross-Dataset Transfer, and Few-Shot Adaptation

Political misinformation spans fact-checked claims and news headlines, yet detection models are often evaluated within a single corpus and input representation. This study presents a metadata-flexible transformer framework evaluated on LIAR2 (19,212 retained claims) and FakeNewsNet-PolitiFact (928 headline-level news items). RoBERTa-base and DeBERTa-v3-base are evaluated using text-only and context-enhanced representations across supervised classification, cross-dataset transfer, few-shot adaptation, and robustness experiments. Across five random seeds, contextual RoBERTa achieves a pooled macro-F1 of 0.7369±0.0072, while DeBERTa with source-withheld context reaches 0.7426±0.0128. On the FakeNewsNet-PolitiFact test partition, full-context DeBERTa achieves a macro-F1 of 0.8842±0.0120, demonstrating strong supervised classification performance. Additional five-seed experiments evaluate robustness under cross-corpus overlap screening, speaker-disjoint partitions, and alternative binary label mappings. These analyses demonstrate that the benefits of contextual information extend across multiple controlled evaluation conditions. Cross-dataset transfer experiments further investigate generalization between political claims and news headlines, revealing differences in model behavior across input representations and data distributions. Five-seed forward-transfer evaluation provides additional evidence concerning the influence of contextual information on cross-corpus performance. In a complementary fixed-seed few-shot experiment, adaptation with 50 labeled target examples per class achieves 0.8480 macro-F1, representing a +0.2653 improvement over zero-shot performance. Matched few-shot experiments further examine the contribution of source-domain initialization under different target-data budgets. Overall, the results demonstrate the effectiveness of context-aware transformer models for political misinformation classification while providing a comprehensive evaluation of their robustness, transferability, and adaptation capabilities. The findings highlight the importance of contextual feature selection and rigorous cross-dataset evaluation for developing more reliable misinformation detection systems.