Reasoning About the Unsaid: Misinformation Detection with Omission-Aware Graph Inference
It addresses omission-based deception in misinformation detection, a largely overlooked problem for readers and fact-checkers, representing a novel direction rather than an incremental improvement.
This paper tackles the problem of detecting misinformation that deceives by omitting important information, presenting OmiGraph, a framework that constructs omission-aware graphs and achieves average improvements of +5.4% F1 and +5.3% ACC on two benchmarks.
This paper investigates the detection of misinformation, which deceives readers by explicitly fabricating misleading content or implicitly omitting important information necessary for informed judgment. While the former has been extensively studied, omission-based deception remains largely overlooked, even though it can subtly guide readers toward false conclusions under the illusion of completeness. To pioneer in this direction, this paper presents OmiGraph, the first omission-aware framework for misinformation detection. Specifically, OmiGraph constructs an omission-aware graph for the target news by utilizing a contextual environment that captures complementary perspectives of the same event, thereby surfacing potentially omitted contents. Based on this graph, omission-oriented relation modeling is then proposed to identify the internal contextual dependencies, as well as the dynamic omission intents, formulating a comprehensive omission relation representation. Finally, to extract omission patterns for detection, OmiGraph introduces omission-aware message-passing and aggregation that establishes holistic deception perception by integrating the omission contents and relations. Experiments show that, by considering the omission perspective, our approach attains remarkable performance, achieving average improvements of +5.4% F1 and +5.3% ACC on two large-scale benchmarks.