A Comprehensive Survey of Multimodal Fake News Detection: Datasets, Methods, and Challenges

Abstract

With the rapid proliferation of multimodal social media platforms, fake news has been increasingly disseminated through multiple modalities such as text, images, and videos, posing serious threats to social stability, public cognition, and these platforms’ ecosystem. Existing unimodal fake news detection methods face much challenge in multimodal scenarios, as they can not capture fully cross-modal semantic correlations and inconsistencies. Multimodal fake news detection, which integrates heterogeneous information from text, visual, and audio modalities to explore inter-modal consistency and complementarity, has therefore become a major research focus in recent years. A comprehensive survey of recent advances in multimodal fake news detection is presented in the paper, which consists of systematically reviews of the fundamental concepts, detection tasks, and underlying technical principles in this field. Major benchmark datasets and commonly used evaluation metrics are also introduced, followed by a structured taxonomy of representative detection methods and a summary of their experimental results. Furthermore, the key challenges faced by current research are discussed and promising future research directions are outlined. Compared with existing surveys, this work presents a more comprehensive method categorization that emphasizes the evolution of detection techniques, offers clearer comparisons of datasets and experimental analyses, and provides more practical insights for researchers and practitioners in multimodal fake news detection.

Keywords

fake news detection / multimodal learning / feature extraction / deep learning

Author Contributions

G.C.: literature research, writing, revision, and supervision. Z.Q.: literature research, writing, and revision. G.B.: literature research and revision. Q.L.: literature research. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China, grant number 62366010, the Guangxi Natural Science Foundation, grant number 2024GXNS-FAA010374, and the Innovation Project of GUET Graduate Education, grant number 2025YCXS048.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study.

Conflicts of Interest

The authors declare no conflict of interest.

Use of AI and AI-Assisted Technologies

During the preparation of this work, the authors used Gemini to polish the language. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.

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