Beyond Words and Pictures: A Survey of Multimodal Fake News Detection

ABSTRACT Multimodal fake news detection (MFND) has attracted growing attention as misinformation increasingly appears in heterogeneous forms that combine text, images, audio, video, and social context, while recent generative models further increase the realism and scalability of deceptive content. Meanwhile, the rise of large language models (LLMs) and multimodal large language models (MLLMs) has introduced new opportunities as well as new evaluation challenges for MFND. In this survey, we present a structured review of the field through a taxonomy that groups existing methods into two broad paradigms: small‐model‐based methods and LLM‐involved methods. For each paradigm, we analyse representative approaches from the perspectives of modelling strategy, factual grounding, robustness, and deployment feasibility. We also review widely used datasets and evaluation protocols, and discuss their limitations with respect to modality composition, task heterogeneity, and result comparability. Finally, we outline important open problems and future directions, including the detection of LLM‐generated misinformation, cross‐lingual generalisation, interpretable and evidence‐grounded reasoning, and trustworthy evaluation in realistic deployment settings.