Multi-modal Rumor Detection in Social Networks: Recent Advances and Future Trends

LI Yuze; LIU Ying

doi:10.12263/DZXB.20260187

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Multi-modal Rumor Detection in Social Networks: Recent Advances and Future Trends

更新时间：2026-06-16

- Multi-modal Rumor Detection in Social Networks: Recent Advances and Future Trends
- ACTA ELECTRONICA SINICA Pages: 1-28(2026)
- 作者机构：
  
  1.西安邮电大学图像与信息处理研究中心，陕西西安 710121
  2.青海理工学院高原生态安全视觉计算联合实验室，青海西宁 810016
- 作者简介：
- 基金信息：
  
  Graduate Innovation Fund of Xi’an University of Posts and Telecommunications(CXJJBDL2024003)
- DOI：10.12263/DZXB.20260187
  CLC： TP391;TP393
- Received：21 April 2026，
  
  Accepted：05 May 2026，
  
  Online First：16 June 2026，
- 稿件说明：
移动端阅览
LI Yuze, LIU Ying. Multi-modal Rumor Detection in Social Networks: Recent Advances and Future Trends[J/OL]. ACTA ELECTRONICA SINICA, 2026, 1-28.
DOI：

LI Yuze, LIU Ying. Multi-modal Rumor Detection in Social Networks: Recent Advances and Future Trends[J/OL]. ACTA ELECTRONICA SINICA, 2026, 1-28. DOI： 10.12263/DZXB.20260187.

摘要

社交网络凭借其即时、开放与传播广泛的特性，在促进信息交流的同时，也沦为谣言滋生的温床。多模态谣言融合文本及图像等多种信息形式，呈现出更强的欺骗性与煽动性，增加了网络治理的难度。因此，实现高效准确的多模态谣言检测，已成为维护清朗网络空间与公共安全的关键任务。近年来，学界针对多模态谣言的数据特性提出了多种检测方法，技术体系日趋多样。为系统梳理该领域当前的研究进展，本文从信息构成与来源视角，构建了一个涵盖内容、外部知识、社交上下文与外部环境四大维度的多模态谣言检测分类体系。区别于现有综述对内容维度的单一侧重，本文通过这一四维分析框架，揭示了谣言检测正从局部的静态内容核查向融合动态社交演化与外部环境交叉验证演进的深层范式转移。在此框架下，深入剖析了基于图文内容交互、外部知识增强、社交上下文信息以及外部环境感知四类技术方法的核心机制、演进脉络与代表性模型，并总结了其技术优势与局限性。此外，本文归纳了常用数据集与评估指标，并从纵向技术演进脉络、跨方法类别统计趋势以及特定检测场景下模型表现三个维度，对比了各类模型的效能差异与适用边界。最后，本文探讨了模态异构与特征对齐瓶颈、外部知识依赖与大语言模型（Large Language Model，LLM）幻觉、动态社会与环境建模复杂性、跨域领域偏差以及高逼真人工智能生成内容（Artificial Intelligence Generated Content，AIGC）谣言鉴伪等五大关键挑战，并对未来的研究方向进行展望。

Abstract

With characteristics of instantaneity

openness

and widespread dissemination

social networks facilitate information exchange but have simultaneously become a hotbed for the proliferation of rumors. Integrating heterogeneous information forms such as text and images

multimodal rumors exhibit greater deceptiveness and provocativeness

thereby significantly complicating network governance. Consequently

achieving efficient and accurate multimodal rumor detection has emerged as a critical mission for maintaining a clean cyberspace and ensuring public safety. In recent years

the research community has proposed diverse detection methodologies tailored to the data characteristics of multimodal rumors

leading to an increasingly diversified technical landscape. To systematically review current research

this paper constructs a multimodal rumor detection taxonomy from the perspective of information composition and sources

encompassing four dimensions: content

external knowledge

social context

and external environment. Unlike existing reviews with a singular emphasis on the content dimension

this paper

through its four-dimensional analytical framework

reveals a profound paradigm shift in rumor detection from localized static content verification to an integrated approach that incorporates dynamic social evolution and external environmental cross-validation. Within this framework

it analyzes the core mechanisms

evolutionary trajectories

and representative models of four methodological categories: visual-textual interaction

external knowledge enhancement

social context information

and external environment perception

summarizing their technical advantages and limitations. Additionally

this paper summarizes commonly used datasets and evaluation metrics

and comparatively analyzes the performance disparities of various models from the three dimensions of longitudinal technical evolution trajectories

cross-category statistical trends

and model performance under specific detection scenarios. Finally

this paper discusses five critical challenges: bottlenecks in modality heterogeneity and feature alignment

risks of external knowledge dependence and large language model (LLM) hallucinations

complexities of dynamic social and environmental modeling

cross-domain biases

and the authentication of highly realistic artificial intelligence generated content (AIGC) rumors. Furthermore

it outlines future research directions.

关键词

Keywords

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GAO Shou-wan

CHEN Peng-peng

ZHANG Qi-liang

ZHANG Kui-yuan

CHEN Jian

SU Si-jiao

HUANG Li-qin

ZHAO Tie-song

Related Institution

Mine Digitization Engineering Research Center of the Ministry of Education

School of Computer Science and Technology/School of Artificial Intelligence, China University of Mining and Technology

Shenzhen Research Institute, China University of Mining and Technology

Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information

College of Physics and Information Engineering, Fuzhou University

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