  Previous studies on multimodal fake news detection have observed the mismatch
between text and images in the fake news and attempted to explore the
consistency of multimodal news based on global features of different
modalities. However, they fail to investigate this relationship between
fine-grained fragments in multimodal content. To gain public trust, fake news
often includes relevant parts in the text and the image, making such multimodal
content appear consistent. Using global features may suppress potential
inconsistencies in irrelevant parts. Therefore, in this paper, we propose a
novel Consistency-learning Fine-grained Fusion Network (CFFN) that separately
explores the consistency and inconsistency from high-relevant and low-relevant
word-region pairs. Specifically, for a multimodal post, we divide word-region
pairs into high-relevant and low-relevant parts based on their relevance
scores. For the high-relevant part, we follow the cross-modal attention
mechanism to explore the consistency. For low-relevant part, we calculate
inconsistency scores to capture inconsistent points. Finally, a selection
module is used to choose the primary clue (consistency or inconsistency) for
identifying the credibility of multimodal news. Extensive experiments on two
public datasets demonstrate that our CFFN substantially outperforms all the
baselines.
