BSOR: Balanced Severity-Aware Ordinal Regression for Fine-Grained Misinformation Detection

The spread of misinformation on social media has become a growing societal concern, making its automatic detection an important yet challenging problem. In practice, misinformation deviates from the truth to varying degrees, and fact-checking organizations therefore grade claims on ordered scales such as the six-level PolitiFact rating. Existing multi-class detection methods, however, treat these ordered grades as independent flat labels, overlooking the ordinal structure among them and consequently suffering from severe prediction deviation. To address this limitation, we propose Balanced Severity-Focused Ordinal Regression (BSOR), a plug-and-play output head that formulates multi-class misinformation detection as ordinal regression. BSOR builds on a conditional decomposition of the ordinal task and further introduces cost optimization based on misjudgment distance awareness and hierarchy equilibrium based on gradient normalization to align the training objective with the severity structure of the task, together with a KL-divergence-constrained soft-label regularizer for stability. Across two public datasets, replacing the flat head with BSOR consistently reduces MAE for nine general-purpose encoders and six representative detection methods, with maximum reductions of 6.99% and 7.94%, respectively. Through error-distribution analysis, we find that the improvement mainly comes from suppressing costly long-range mispredictions. Representation-level analysis further shows that the gains stem from a more sufficient learning of the ordinal relations implicit in the data. These results demonstrate that BSOR offers plug-and-play generality and achieves stable performance gains in fine-grained misinformation detection.