通过补全被省略信息，提升对隐蔽误导内容的识别能力
Reasoning About the Unsaid: Misinformation Detection with Omission-Aware Graph Inference
- 构建含互补视角的图结构，挖掘新闻中缺失的关键信息
- 在两个大型数据集上实现F1提升5.4%、准确率提升5.3%
- 适合关注隐性虚假信息检测的研究者与平台审核人员
本文研究误导性信息检测，这类信息通过显性造假或隐性省略关键信息来误导读者。尽管显性造假已得到广泛研究，但隐性省略仍被忽视，其可看似完整地引导读者得出错误结论。为此，本文提出首个面向省略的检测框架OmiGraph。该框架通过构建包含同一事件多元视角的上下文环境，生成含省略感知的图结构，揭示潜在缺失内容。在此基础上，设计省略导向的关系建模，捕捉内部语境依赖与动态省略意图，形成综合的省略关系表征。最后，引入省略感知的消息传递与聚合机制，整合省略内容与关系，实现全局性欺骗感知。实验表明，考虑省略视角后，该方法在两个大规模基准上分别取得平均5.4%的F1提升和5.3%的准确率提升。
原文摘要 · Abstract (English)
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.
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