Multimodal misinformation on online social platforms is becoming a critical
concern due to increasing credibility and easier dissemination brought by
multimedia content, compared to traditional text-only information. While
existing multimodal detection approaches have achieved high performance, the
lack of interpretability hinders these systems' reliability and practical
deployment. Inspired by NeuralSymbolic AI which combines the learning ability
of neural networks with the explainability of symbolic learning, we propose a
novel logic-based neural model for multimodal misinformation detection which
integrates interpretable logic clauses to express the reasoning process of the
target task. To make learning effective, we parameterize symbolic logical
elements using neural representations, which facilitate the automatic
generation and evaluation of meaningful logic clauses. Additionally, to make
our framework generalizable across diverse misinformation sources, we introduce
five meta-predicates that can be instantiated with different correlations.
Results on three public datasets (Twitter, Weibo, and Sarcasm) demonstrate the
feasibility and versatility of our model.

提出一种新颖的基于逻辑的神经模型，该模型将可解释的逻辑子句集成到目标任务的推理过程中，以检测多模态误解信息，通过在神经表示中参数化符号逻辑元素来实现学习的有效性，并引入五个元预测以使我们的框架具有多样性。