XplaiNLP @ ClimateCheck 2026 Task 2: Comparing Hierarchical Approaches for Fine-Grained Climate Disinformation Narrative Classification

Abstract

We present our submission to Task 2 of the ClimateCheck 2026 shared task on Disinformation Narrative Classification which requires assigning climate-contrarian claims to fine-grained disinformation narratives. Using Qwen3-8B as a fixed backbone, we systematically compare data augmentation, prompt engineering and reinforcement learning techniques. Our experiments show that structured reasoning, particularly a chain-of-thought (CoT) prompting strategy aligned with the taxonomy’s hierarchical structure, substantially improves Macro-F1 over both zero-shot baselines and augmentation-based fine-tuning. Our best configuration achieves ∼0.625 Macro-F1, ranking first in Task 2. Our findings demonstrate that carefully designed hierarchical prompting can rival more complex training interventions in low-resource, highly imbalanced narrative classification settings.

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