Prefix-Conditioned Diffusion for Early Rumor Detection Under Sparse and Incomplete Social Cascades

Early rumor detection predicts verification labels from sparse and incomplete propagation cascades. We introduce RumorDiffusion, a prefix-conditioned framework that combines class-conditional topology diffusion with a Transformer arbiter. The generator learns topology-and-feature distributions from mature training cascades. At inference, it uses the observed prefix to generate a potential vector for each class hypothesis, and the arbiter converts the resulting vectors into verification probabilities. A two-stage training scheme pretrains the generator, then freezes it while the arbiter learns discriminative aggregation. Repeated stochastic prediction supplies averaged probabilities and a disagreement statistic. Validation-based temperature scaling and confidence thresholds support selective early decisions from these predictions. Experiments on Twitter15, Twitter16, Weibo, and PHEME evaluate classification, component contributions, calibration, sampling cost, and source-only transfer across early observation windows. RumorDiffusion achieves the highest accuracy and Macro-F1 in three of four main comparisons and nine of twelve early dataset-window comparisons. Its main-comparison Macro-F1 reaches 0.838 on Twitter15, 0.854 on Twitter16, and 0.908 on Weibo. Component ablations quantify the contribution of class-conditioned representations. Temperature scaling reduces expected calibration error (ECE) while preserving Macro-F1. RumorDiffusion also achieves the highest transfer Macro-F1 and lowest ECE in both evaluated domain pairs. These results establish prefix-conditioned generation as an effective basis for calibrated early verification. Our code is available at https://github.com/cgao-comp/RumorDiffusion .