Multimodal Feature Adaptive Fusion for Fake News Detection.

In order to solve the problem that it is difficult to make full use of graphic and text information in multimodal news detection in social media news and to explore efficient multimodal information interaction methods, an adaptive multimodal feature fusion model for fake news detection is proposed. First, the model extracts and represents news text semantic features, text emotional features, and image-text semantic difference features; then, weighted splicing and fusion of various features are performed by adding adaptive weight parameters to reduce the redundancy introduced by model splicing; finally, the fusion feature is sent to the classifier. Experimental results show that the proposed model outperforms the current state-of-the-art models in evaluation indicators such as F1 score. It effectively improves the performance of fake news detection and provides strong support for the detection of fake news in social media. [ABSTRACT FROM AUTHOR]