Reasoning About the Unsaid: Misinformation Detection with Omission-Aware Graph Inference

2025-12-01

AI Summary

This study addresses the detection of implicit misleading fake news caused by critical information omissions—a gap in existing work that predominantly focuses on explicit fabrication while neglecting implicit deception. To this end, we propose OmiGraph, the first omission-aware graph neural network framework: it constructs an omission-aware graph structure to model dynamic omission intent, and introduces a completion-aware message-passing mechanism that jointly performs contextual modeling, omission relation representation learning, and multi-perspective information completion for robust reasoning. Evaluated on two large-scale benchmarks, OmiGraph achieves average improvements of 5.4% in F1-score and 5.3% in accuracy over state-of-the-art methods, demonstrating both the effectiveness and necessity of explicitly modeling information omission for fake news detection.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Application Domains: Misinformation & Fake News
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Detects misinformation by identifying omitted information in news
• Models omission intents and contextual dependencies for deception detection
• Improves detection accuracy using omission-aware graph inference framework

Innovation

• Omission-aware graph construction for news
• Omission-oriented relation modeling for intents
• Omission-aware message-passing for deception perception