Interpretable Multimodal Misinformation Detection with Logic Reasoning
- URL: http://arxiv.org/abs/2305.05964v1
- Date: Wed, 10 May 2023 08:16:36 GMT
- Title: Interpretable Multimodal Misinformation Detection with Logic Reasoning
- Authors: Hui Liu, Wenya Wang, Haoliang Li
- Abstract summary: We propose a novel logic-based neural model for multimodal misinformation detection.
We parameterize symbolic logical elements using neural representations, which facilitate the automatic generation and evaluation of meaningful logic clauses.
Results on three public datasets demonstrate the feasibility and versatility of our model.
- Score: 31.97249246223621
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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