LATENT CAUSAL VOID: EXPLICIT MISSING-CONTEXT RECONSTRUCTION FOR MISINFORMATION DETECTION

Automatic misinformation detection performs well when deception is visible in what an article explicitly states. However, some misinformation articles remain locally coherent and only become misleading once compared with contemporaneous reports that supply background context the article omits. We study this omission-relevant setting and observe that current omission-aware approaches typically either attach retrieved context as auxiliary evidence or infer a categorical omission signal, leaving the specific omitted content implicit. We propose Latent Causal Void (LCV), a retrieval-guided detector that explicitly reconstructs the omitted content element for each target sentence and uses it as a textual cross-source relation in graph reasoning. Crucially, we do not treat the retrieved environment as verified ground truth: we formalize the learning signal as an environment-relative discrepancy between a target and its contemporaneous information environment, which is consistent with how that environment was originally constructed. Concretely, LCV retrieves temporally aligned context articles, asks a frozen instruction-tuned large language model to generate a short missing-context description for each sentence–article pair, and feeds the resulting relation text into a heterograph over target sentences and context articles. On the bilingual benchmark of Sheng et al., under a matched protocol with an identical local reimplementation of the strongest omission-aware baseline, LCV improves macro-F1 by $2.94$ and $2.77$ points on the English and Chinese splits. We further audit $600$ reconstructed relations by hand, finding $87\%$ source support; among the $332$ externally verifiable relations, $75\%$ are independently corroborated. We stress-test the detector under four families of controlled retrieval corruption, and compare against a strong non-graph consumer of the same reconstructed text. The results indicate that modeling the omitted cross-source content itself, rather than only attaching retrieve