Rumor spreaders are increasingly utilizing multimedia content to attract the
attention and trust of news consumers. Though quite a few rumor detection
models have exploited the multi-modal data, they seldom consider the
inconsistent semantics between images and texts, and rarely spot the
inconsistency among the post contents and background knowledge. In addition,
they commonly assume the completeness of multiple modalities and thus are
incapable of handling handle missing modalities in real-life scenarios.
Motivated by the intuition that rumors in social media are more likely to have
inconsistent semantics, a novel Knowledge-guided Dual-consistency Network is
proposed to detect rumors with multimedia contents. It uses two consistency
detection subnetworks to capture the inconsistency at the cross-modal level and
the content-knowledge level simultaneously. It also enables robust multi-modal
representation learning under different missing visual modality conditions,
using a special token to discriminate between posts with visual modality and
posts without visual modality. Extensive experiments on three public real-world
multimedia datasets demonstrate that our framework can outperform the
state-of-the-art baselines under both complete and incomplete modality
conditions. Our codes are available at this https URL

提出一种基于知识引导的双一致性网络，用于检测具有多媒体内容的谣言，该网络具有两个一致性检测子网络，可同时捕获跨模态和内容 - 知识水平的不一致性，并在不同丢失视觉模态条件下实现鲁棒的多模态表示学习。该框架在三个公共实际多媒体数据集上的广泛实验表明，可以在完整和不完整的多模态条件下优于最先进的基线。