{Vosoughi18} Soroush Vosoughi, Deb Roy, Sinan Aral. The spread of true and false news online. Science, 2018, 359(6380):1146-1151.
{Ma17} Jing Ma, Wei Gao, Kam-Fai Wong. Detect rumors in microblog posts using propagation structure via kernel learning. Proc. ACL. 2017. provide valuable clues on how an original message is transmitted and developed over time
{Xiao23} Yunpeng Xiao, Xuehong Li, Qunqing Zhang, Rui Lv, Qian Li, Rong Wang. Spreading Mosaic: An Image Restoration-Inspired Social Rumor Propagation Model. IEEE Transactions on Multimedia, 2023, 26:2906-2917. this study proposes a rumor propagation prediction model based on image restoration technology
{Pröllochs23} Nicolas Pröllochs and Stefan Feuerriegel. Mechanisms of True and False Rumor Sharing in Social Media: Collective Intelligence or Herd Behavior? Proc. ACM Hum.-Comput. Interact., 2023, 7(CSCW2):Article 287. Based on 126,301 Twitter cascades, we find that the sharing behavior is characterized by lifetime and crowd effects that explain differences in the spread of true as opposed to false rumors
{Chan23} Man-pui Sally Chan, Dolores Albarracin. A meta-analysis of correction effects in science-relevant misinformation. Nature Human Behaviour. 2023, 7(9): 1514-1525. examine whether science-related misinformation can be corrected and what factors influence the effectiveness of such corrections. The conclusion is that, overall, it is difficult to correct such misinformation, but efforts are more likely to succeed when dealing with negative, non-polarizing topics that are familiar to the audience.
{Xiao25} Yunpeng Xiao, Jinsong Yang, Wanjing Zhao, Qian Li, Yucai Pang. Cross-Domain Social Rumor-Propagation Model Based on Transfer Learning. IEEE Transactions on Neural Networks and Learning Systems, 2025, 36(4):6529-6543. To resolve the scarce-data problem in some rumor-topic domains, this study proposes a cross-domain rumor-propagation model, which is based on transfer learning
{Oliveira26} Kleber Andrade Oliveira, Pietro Traversa, Guilherme Ferraz de Arruda, Yamir Moreno. Rumor propagation on hypergraphs. Nature Communications, 2026. this study proposes a higher-order rumor propagation model based on hypergraphs to characterize the mechanism of propagation triggered within a group, diffusion between groups, and the cessation of propagation by spreaders once the group becomes aware of the information.
Rumor Detection
{Ma16} Jing Ma, Wei Gao, Prasenjit Mitra, Sejeong Kwon, Bernard J. Jansen, Kam-Fai Wong, Meeyoung Cha. Detecting Rumors from Microblogs with Recurrent Neural Networks. Proc. of IJCAI, 2016. RNN-based method detects rumors more quickly and accurately than existing techniques
{Kochkina17} Elena Kochkina, Maria Liakata, Arkaitz Zubiaga. All-in-one: Multi-task learning for rumour verification. Proc. COLING. 2018. provide valuable clues on how an original message is transmitted and developed over time
{Chen19} Yixuan Chen, Jie Sui, Liang Hu, Wei Gong. Attention-Residual Network with CNN for Rumor Detection. Proc. of CIKM, 2019.
{Tam19} Nguyen Thanh Tam, Matthias Weidlich, Bolong Zheng, Hongzhi Yin, Nguyen Quoc Viet Hung, and Bela Stantic. From anomaly detection to rumour detection using data streams of social platforms. Proc. VLDB Endow., 2019, 12(9):1016–1029. PDFa novel approach to rumour detection that identifies anomalies on social platforms by comparing data between peers and with the past
{Xia20} Rui Xia, Kaizhou Xuan, and Jianfei Yu. A state-independent and time-evolving network for early rumor detection in social media. Proc. EMNLP., 2020: 9042-9051. propose a state-independent and time-evolving Network (STN) for rumor detection based on fine-grained event state detection and segmentation
{Song21} Changhe Song, Cheng Yang , Huimin Chen, Cunchao Tu , Zhiyuan Liu , and Maosong Sun. CED: Credible early detection of social media rumors. IEEE Transactions on Knowledge and Data Engineering, 2019, 33(8): 3035-3047. present a novel early rumor detection model, Credible Early Detection (CED). By regarding all reposts to a rumor candidate as a sequence, the proposed model will seek an early point-in-time for making a credible prediction.
{Sun22} Mengzhu Sun, Xi Zhang, Jiaqi Zheng, Guixiang Ma. DDGCN: Dual Dynamic Graph Convolutional Networks for Rumor Detection on Social Media. Proc. of AAAI, 2022. model the dynamics of messages in propagation as well as the dynamics of the background knowledge from Knowledge graphs in one unified framework
{Naumzik22} Christof Naumzik and Stefan Feuerriegel. Detecting False Rumors from Retweet Dynamics on Social Media. Proc. of WWW, 2022. develop a novel probabilistic mixture model that classifies true vs. false rumors based on the underlying spreading process
{Huang22} Zhen Huang, Zhilong Lv, Xiaoyun Han, Binyang Li, Menglong Lu, Dongsheng Li. Social Bot-Aware Graph Neural Network for Early Rumor Detection. Proc. of COLING, 2022. aims at early rumor detection by accounting for social bots’ behavior, and presents a Social Bot-Aware Graph Neural Network, named SBAG.
{Zeng22} Fengzhu Zeng, and Wei Gao. Social Bot-Aware Graph Neural Network for Early Rumor Detection. Proc. of ACL, 2022: 4105-4117. construct BEARD, a new Benchmark dataset for EARD, based on claims from fact-checking websites by trying to gather as many early relevant posts as possible. And propose HEARD, a novel model based on neural Hawkes process for EARD, which can guide a generic rumor detection model to make timely, accurate and stable predictions.
{Hu23} Xuming Hu, Zhijiang Guo, Junzhe Chen, Lijie Wen, and Philip S. Yu. MR2: A Benchmark for Multimodal Retrieval-Augmented Rumor Detection in Social Media. Proc. of ACM SIGIR, 2023. a multimodal multilingual retrieval-augmented dataset for rumor detection.
{Lin23} Hongzhan Lin, Pengyao Yi, Jing Ma, Haiyun Jiang, Ziyang Luo, Shuming Shi, Ruifang Liu. Zero-Shot Rumor Detection with Propagation Structure via Prompt Learning. Proc. of AAAI, 2023. propose a novel zero-shot framework based on prompt learning to detect rumors falling in different domains or presented in different languages
{Yang23} Chang Yang, Peng Zhang, Wenbo Qiao, Hui Gao, Jiaming Zhao. Rumor Detection on Social Media with Crowd Intelligence and ChatGPT-Assisted Networks. Proc. of EMNLP, 2023. propose a Crowd Intelligence and ChatGPT-Assisted Network(CICAN) for rumor classification
{Zhang24} Huaiwen Zhang, Xinxin Liu, Qing Yang, Yang Yang, Fan Qi, Shengsheng Qian, and Changsheng Xu. T3RD: Test-Time Training for Rumor Detection on Social Media. Proc. of WWW, 2024. introduce the Test-Time Training for Rumor Detection (T3RD) to enhance the performance of rumor detection models on low-resource datasets
{Guo25} Mei Guo, Chen Chen, Chunyan Hou, Yike Wu, Xiaojie Yuan. SWAM: Adaptive Sliding Window and Memory-Augmented Attention Model for Rumor Detection. Proc. of EMNLP, 2025. propose a novel adaptive Sliding Window and memory-augmented Attention Model (SWAM) for rumor detection. The adaptive sliding window divides the sequence of posts into consecutive disjoint windows based on the propagation rate of nodes.
{Zhang25} Mingqing Zhang, Qiang Liu, Xiang Tao, Shu Wu1, Liang Wang. SINCon: Mitigate LLM-Generated Malicious Message Injection Attack for Rumor Detection. Proc. of ACL, 2025. propose Similarizing the predictive Influence of Nodes with Contrastive Learning, a defense mechanism that encourages the model to learn graph representations where nodes with varying importance have a more uniform influence on predictions.
{Tian25} Zhiliang Tian, Jingyuan Huang, Zejiang He, Zhen Huang, Menglong Lu, Linbo Qiao, Songzhu Mei, Yijie Wang, Dongsheng Li. LLM-based rumor detection via influence guided sample selection and game-based perspective analysis. Proc. of ACL. 2025: 28402-28414. propose conducting high-quality sample screening at the sample level and multi-perspective analysis and screening at the analysis level, and finally using the screened samples and analysis results together to fine-tune the LLM.
{Wang25} Bing Wang, Bingrui Zhao, Ximing Li, Changchun Li, Wanfu Gao, Shengsheng Wang. Collaboration and Controversy Among Experts: Rumor Early Detection by Tuning a Comment Generator. Proc. of SIGIR. 2025: 468-478. integrate a mixture-of-expert structure into a generative language model and present a novel routing network for expert collaboration.
{Xu25} Haowei Xu, Chao Gao, Xianghua Li, and Zhen Wang, Shengsheng Wang. D2: Customizing two-stage graph neural networks for early rumor detection through cascade diffusion prediction. Proc. of WSDM. 2025: 568-576. propose D2, a two-stage framework for early rumor Detection, integrating cascade Diffusion prediction.
{Zeng26} Fengzhu Zeng, Qian Shao, Ling Cheng, Wei Gao, Shih-Fen Cheng, Jing Ma, and Cheng Niu. LLM-based Few-Shot Early Rumor Detection with Imitation Agent. Proc. of ACM KDD, 2026. propose a novel EARD framework that combines an autonomous agent and an LLM-based detection model, where the agent acts as a reliable decision-maker for early time point determination, while the LLM serves as a powerful rumor detector.
Rumor Suppression
{Yang22} Wenjie Yang, Sitong Wang, Zhenhui Peng, Chuhan Shi, Xiaojuan Ma, Diyi Yang. Know It to Defeat It: Exploring Health Rumor Characteristics and Debunking Efforts on Chinese Social Media during COVID-19 Crisis. Proc. of AAAI ICWSM, 2022. explore how four kinds of social media users (i.e., government, media, organization, and individual) combat health rumors, and identify their preferred way of sharing the debunking information and the key rhetoric strategies used in the process
{Yang23} Lan Yang, Ziyue Ma, Zhiwu Li, Alessandro Giua. Rumor Containment by Blocking Nodes in Social Networks. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023, 53(7):3990-4002. present a mathematical programming formulation based on integer linear programming (ILP) to minimize rumor spread by blocking a subset of nodes (called blockers) in complex social networks modeled as a linear threshold model
{Gao24} Fei Gao, Qiang He, Xingwei Wang, Lin Qiu, Min Huang. An Efficient Rumor Suppression Approach With Knowledge Graph Convolutional Network in Social Network. IEEE Transactions on Computational Social Systems, 2024, 11(5):6254-6267. propose the two-stage user opinion rumor propagation model (TSUORP), which fully incorporates the timing of official releases of rumor-refuting information and their influence on the generation of rumors propagation
{Gao24} Fei Gao, Xingwei Wang, Qiang He. Multi-Feature Rumor Suppression Mechanism Based on Community Division in Social Networks. IEEE Transactions on Network Science and Engineering, 2024, 11(2):2047-2061. proposes the Multi-feature Rumor Propagation Model (MFRPM) and defines the problem of minimizing the influence of multi-feature rumors
{He25} Qiang He, Zelin Zhang, Tingting Bi, Hui Fang, Xiushuang Yi, and Keping Yu. Adaptive Rumor Suppression on Social Networks: A Multi-Round Hybrid Approach. ACM Trans. Knowl. Discov. Data., 2025, 19(2):Article 31. introduce the concept of Adaptive Rumor Suppression (ARS), which aims to dynamically counter rumors by taking into account the nuances of propagation dynamics and the surrounding environmental context
{Zhong25} Xiaojing Zhong, Jing Zhang, Aojing Wang, Guiyun Liu, Feiqi Deng, Jianhui Wang. Rumor Suppression in a Three-Layer Network: A Reinforcement Learning Algorithm. IEEE Transactions on Network Science and Engineering, 2025, 12(3):2292-2307. constructs a three-layer network rumor control model (SICR-3M3W) that considers the dual refutation mechanism and formulates an optimal control problem for this model
{He26} Qiang He, Zhen Tang, Runze Jiang, Zelin Zhang, Hui Fang, Xingwei Wang, Lianbo Ma, and Keping Yu. Uncertainty Rumor Blocking in Social Networks: A Graph Inverse Reinforcement Learning Approach. IEEE Transactions on Networking, 2026. introduce the concept of Uncertainty Rumor Blocking, where they address the uncertainty surrounding rumor node locations by considering a set of suspicious nodes, each associated with a probability indicating the likelihood of rumor propagation.
Survey
{Pattanaik23} Barsha Pattanaik, Sourav Mandal, Rudra M. Tripathy. A survey on rumor detection and prevention in social media using deep learning. Knowledge and information systems, 2023, 65(10): 3839-3880.
{Sattarov25} Otabek Sattarov and Jaeyoung Choi. Detection of rumors and their sources in social networks: A comprehensive survey. IEEE Transactions on Big Data, 2024, 11(3): 1528-1547.