Abstract: In recent years,social media has become a fertile ground for the spread and proliferation of fake news due to its openness and convenience.Compared to unimodal fake news,multimodal fake news,which combines various forms of information such as text and images,creates more confusing false content and has a more far-reaching effects.Existing methods for multimodal fake news detection predominantly rely on small models.However,the rapid development of multimodal large models offers new pers-pectives for addressing this issue.These models,though,are typically parameter-intensive and computationally demanding,making them challenging to deploy in environments with limited computational and energy resources.To address these challenges,this study proposes a multimodal fake news detection model based on the multimodal large model Long-CLIP.This model is capable of processing long texts and attending to both coarse-grained and fine-grained details.Additionally,by employing an efficient coarse-to-fine layer-wise pruning method,a more lightweight multimodal fake news detection model is obtained to adapt to resource-constrained scenarios.Finally,on the Weibo dataset,the proposed model is compared with current popular multimodal large models before and after fine-tuning and other pruning methods,and its effectiveness is verified.Results indicate that the Long-CLIP-based multimodal fake news detection model significantly reduces model parameters and inference time compared to current popular multimodal large models,while maintaining superior detection performance.After compression,the model achieves a 50% reduction in parameters and a 1.92 s decrease in inference time,with only a 0.01 drop in detection accuracy.

Key words: Fake news detection, Multimodal large models, Resource-constrained, Model compression, Pruning