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

Yi Tang et al.

International Journal of Web Based Communities2025https://doi.org/10.1504/ijwbc.2025.145141article
ABDC C
Weight
0.50

What the paper says

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.

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https://doi.org/https://doi.org/10.1504/ijwbc.2025.145141

Or copy a formatted citation

@article{yi2025,
  title        = {{False information recognition of social media platforms based on multi-modal feature fusion}},
  author       = {Yi Tang et al.},
  journal      = {International Journal of Web Based Communities},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijwbc.2025.145141},
}

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

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.