Work

Multimodal social media disinformation detection by fusing images and BERT

Year: 2025

Type: article

Abstract: To address the diversity and fragmentation issues in false information detection (FID) on social media, this article proposed the MM-GCN-BERT model based on the advantages of GCN and BERT models, integrating multimodal fusion and cascading detection frameworks. By designing and optimising the MM-GCN-BERT model, and combining specific experimental data to verify the performance of the MM-GCN-BERT model, experimental results were obtained. The results showed that the average F1 value of the MM-GCN-BERT model was 0.816, the average precision was 0.836, and the average recall was 0.797. The indicators of the model are all at a high level. It can be seen that the MM-GCN-BERT model has shown excellent performance in solving the problems of diversity and fragmentation in detecting false information on social media.

Source: International Journal of Intelligent Information and Database Systems

DOI: 10.1504/ijiids.2025.147437

Authors: Lizhu Ye, Md Gapar Md Johar, Mohammed Hazim Alkawaz

Institutions: Management and Science University, Guangzhou College of Commerce, University of Mosul

Language: English

Topic: Misinformation and Its Impacts

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

Open Access status: diamond

OpenAlex ID: W4412601913

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