# False Information Detection via Multimodal Feature Fusion and Multi-Classifier Hybrid Prediction
## 5 Conclusions  
The detection model proposed in this paper has the following advantages: (1) It can interact with the data of different modalities in the early stage to exploit its correlation; (2) It can remove the influence of noise during the fusion process; (3) It can reduce the loss of information in the fusion process; (4) The post-fusion module enables the model to handle the asynchrony of the data, allowing different modalities to use their most appropriate analysis method. At the same time, it contains several disadvantages: (1) The remaining information, other than text and images, cannot be used; (2) The extraction of features is difficult. The features of different modalities must have the same format. It is difficult to express the time synchronization between multimodal features, and it is difficult to obtain the cross-correlation between features as the number of features increases; (3) The model structure is complex and the training is difficult; (4) Only the overall fusion is performed on the data of different modalities, and the local fusion is not performed.  
This paper proposes a model for false information detection using multimodal information. The model uses a hybrid fusion method to fuse multimodal information, firstly using a deep autoencoder as an early fusion model to obtain multimodal joint features, and the loss of original information in the fusion process is reduced by concatenating text features and image features on the joint features. Finally, the multimodal data are further fused by the late fusion method. The experimental results on Weibo dataset and Twitter dataset show that the detection accuracy of our model is better than the baseline model. At the same time, the model proposed in this paper has limitations: (1) For a post containing multiple pictures, only one of the pictures can be used to detect it, and all the pictures cannot be used at the same time; (2) The complex structure of the model has a large number of parameters and cannot be run on small devices. The following issues should be addressed in future work: (1) Reduce the complexity of the model so that it can be applied to small devices; (2) Use all the information in the post to detect it; (3) Detect false information that has begun to spread on social platforms.  
Conceptualization, Y.L.; methodology, Y.L.; software, T.T. and A.H.; validation, Y.L., T.T. and A.H.; formal analysis, Y.L.; investigation, Y.L. and T.T.; resources, T.T.; data curation, T.T.; writing--original draft preparation, Y.L.; writing--review and editing, Y.L. and T.T.; visualization, Y.L.; supervision, T.T.; project administration, T.T. and A.H.; funding acquisition, T.T. and A.H. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the National Natural Science Foundation of China (62166042, U2003207), Natural Science Foundation of Xinjiang, China (2021D01C076), and Strengthening Plan of National Defense Science and Technology Foundation of China (2021-JCJCJ-J)-0059).  
Not applicable.  
**Data Availability Statement:** Twitter dataset: [[https://github.com/MKLab-ITI/image-verification-corpus](https://github.com/MKLab-ITI/image-verification-corpus)]([https://github.com/MKLab-ITI/image-verification-corpus](https://github.com/MKLab-ITI/image-verification-corpus)). Weibo dataset: [[https://drive.google.com/file/d/14](https://drive.google.com/file/d/14) VQ7 EWPiFeGzxp3 XC2 DeEHi-BEisDIN/view?usp=sharing]([https://drive.google.com/file/d/14](https://drive.google.com/file/d/14) VQ7 EWPiFeGzxp3 XC2 DeEHi-BEisDIN/view?usp=sharing) (accessed on 27 February 2022).  
The authors declare no conflict of interest.