Title: False information recognition of social media platforms based on multi-modal feature fusion

Authors: Yi Tang; Jiaojun Yi; Feigang Tan

Addresses: School of Information Technology and Engineering, Guangzhou College of Commerce, GuangZhou, 511363, China ' School of Economics, Guangzhou College of Commerce, Guangzhou, 511363, China ' School of Traffic and Environment, Shen Zhen Institute of Information Technology, Shenzhen, 518172, China

Abstract: Traditional social media platforms have low accuracy in identifying false information. Therefore, a method based on multi-modal feature fusion is proposed to recognise false information within social media platforms. This method processes false information data on social media platforms by calculating noise during transmission, and utilises multi-layer management to establish correlations between multi-modal point cloud data. By designing modal grouping and calculating similarity, we integrate information from the three dimensions of time, space, and attributes to supplement the shortcomings of the data. By utilising multi-modal feature fusion algorithms, accurate recognition of false information on social media platforms can be achieved. The experimental results show that using this method can effectively improve the training accuracy of the model and have the ability to resist false data injection attacks, achieving high recognition accuracy.

Keywords: multi-modal feature fusion; social media platform; false information; recognition methods.

DOI: 10.1504/IJWBC.2025.145141

International Journal of Web Based Communities, 2025 Vol.21 No.1/2, pp.78 - 90

Received: 19 Jul 2023
Accepted: 07 Nov 2023
Published online: 21 Mar 2025 *

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