社交媒体谣言检测：方法、挑战与趋势

doi:10.3778/j.issn.1002-8331.2407-0550

摘要/Abstract

摘要： 随着社交媒体的迅速发展，谣言的传播对网络环境和社会秩序有着不容忽视的影响。因此，开展谣言检测研究，阻止其传播，对维持社会和谐至关重要。系统地梳理了近年来谣言检测领域的研究工作进展，阐释了谣言的定义以及相关概念的区别与联系；列举了谣言检测的常用数据集并重点对文本数据集进行分析，探讨了包括监督学习、半监督学习、无监督学习、少样本学习和零样本学习在内的多种机器学习范式策略的谣言检测方法；创新性地讨论了大模型在谣言检测中的应用，并综合介绍了图像、视频等多模态数据在谣言检测中的处理方法和相关研究。最后，对谣言检测面临的挑战以及未来可能的发展方向进行了探讨。

关键词: 社交媒体, 谣言检测, 多模态, 大模型

Abstract: With the rapid development of social media, the spread of rumors has an impact on the network environment and social order that cannot be ignored. Therefore, it is very important to carry out research on rumor detection and prevent its spread to maintain social harmony. This paper systematically reviews the research progress in the field of rumor detection in recent years. Firstly, the definition of rumor and the differences and connections of related concepts are explained. Secondly, the common data sets of rumor detection are listed and text data sets are analyzed, and the rumor detection methods of various machine learning paradigm strategies are discussed, including supervised learning, semi-supervised learning, unsupervised learning, small sample learning and zero sample learning. Thirdly, the application of large model in rumor detection is discussed innovatively, and the processing methods of image, video and other multimodal data in rumor detection and related research are introduced comprehensively. Finally, the challenges faced by rumor detection and the possible development direction in the future are discussed.

Key words: social media, rumor detection, multimodality, large model

刘鑫楠, 洪鑫宇, 曹振洋, 李荣荣, 王子硕, 周俊康, 唐斌, 陆恒杨. 社交媒体谣言检测：方法、挑战与趋势[J]. 计算机工程与应用, 2025, 61(11): 31-50.

LIU Xinnan, HONG Xinyu, CAO Zhenyang, LI Rongrong, WANG Zishuo, ZHOU Junkang, TANG Bin, LU Hengyang. Social Media Rumor Detection: Methods, Challenges, and Trends[J]. Computer Engineering and Applications, 2025, 61(11): 31-50.

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