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Early detection of rumors based on source tweet-word graph attention networks

Hao Jia, Honglei Wang, Xiaoping Zhang, Sathishkumar V. E.
2022 PLoS ONE  
Considering the small-world property of social networks, the source tweet-word graph is decomposed from the global graph of rumors, and a rumor detection method based on graph attention network of source  ...  tweet-word graph is proposed to fully learn the structure of rumor propagation and the deep representation of text contents.  ...  propagation graph, combining textual information or user profiles in rumors for rumor detection.  ... 
doi:10.1371/journal.pone.0271224 pmid:35816493 pmcid:PMC9273096 fatcat:xxc34zq5bvcivh25vn6qauvyva

Recurrent Graph Neural Networks for Rumor Detection in Online Forums [article]

Di Huang, Jacob Bartel, John Palowitch
2021 arXiv   pre-print
We train the R-GNN on news link categorization and rumor detection, showing superior results to recent baselines.  ...  Using Reddit as a case-study, we show how to obtain a derived social graph, and use this graph, Reddit post sequences, and comment trees as inputs to a Recurrent Graph Neural Network (R-GNN) encoder.  ...  These platforms have a natural social graph created by users, which provides an inherent graph on which a Graph Neural Network can propagate rumor information.  ... 
arXiv:2108.03548v1 fatcat:dycxxxjz2nhpjiqx27ir5mijla

Heterogeneous Graph Attention Networks for Early Detection of Rumors on Twitter [article]

Qi Huang, Junshuai Yu, Jia Wu, Bin Wang
2020 arXiv   pre-print
In this paper, we construct a tweet-word-user heterogeneous graph based on the text contents and the source tweet propagations of rumors.  ...  for rumor detection.  ...  Subgraph Attention Network Considering that the neighbors of each node in subgraphs have different importance to learn node embedding for rumor detection and inspired by graph attention networks [15]  ... 
arXiv:2006.05866v1 fatcat:jeuekbvdnfezvilcxhlb7fg3xu

Research status of deep learning methods for rumor detection

Li Tan, Ge Wang, Feiyang Jia, Xiaofeng Lian
2022 Multimedia tools and applications  
Besides, this work summarizes 30 works into 7 rumor detection methods such as propagation trees, adversarial learning, cross-domain methods, multi-task learning, unsupervised and semi-supervised methods  ...  , based knowledge graph, and other methods for the first time.  ...  has used graph structure adversarial learning for the learning task of propagating graphs, they only consider the detection of abnormal propagation points to help the rumor detection task.  ... 
doi:10.1007/s11042-022-12800-8 pmid:35469150 pmcid:PMC9022167 fatcat:h5vjukpkyzdhnjhikgtpj347e4

Rumor Detection Based On Propagation Graph Neural Network With Attention Mechanism

Zhiyuan Wu, Dechang Pi, Junfu Chen, Meng Xie, Jianjun Cao
2020 Expert systems with applications  
On this basis, we propose two models, namely GLO-PGNN (rumor detection model based on the global embedding with propagation graph neural network) and ENS-PGNN (rumor detection model based on the ensemble  ...  We first propose a novel way to construct the propagation graph by following the propagation structure (who replies to whom) of posts on Twitter.  ...  Acknowledgments The research work is supported by National Natural Science Foundation of China (U1433116) and the Fundamental Research Funds for the Central Universities (NP2017208).  ... 
doi:10.1016/j.eswa.2020.113595 pmid:32565619 pmcid:PMC7274137 fatcat:pkowkwq5anckfdhc3guh6jer4e

Catch me if you can: A participant-level rumor detection framework via fine-grained user representation learning

Xueqin Chen, Fan Zhou, Fengli Zhang, Marcello Bonsangue
2021 Information Processing & Management  
In this study, we propose a novel participantlevel rumor detection framework.  ...  Practically, our results can be used to improve the quality of rumor detection services for social platforms.  ...  Acknowledgments This work was supported by National Natural Science Foundation of China (Grant No. 62072077 and No. 61602097), Sichuan Regional Innovation Cooperation Project (Grant No. 2020YFQ0018).  ... 
doi:10.1016/j.ipm.2021.102678 fatcat:2qvoyb4zkzdihef2bc2zkepny4

Microblog-HAN: A micro-blog rumor detection model based on heterogeneous graph attention network

Bei Bi, Yaojun Wang, Haicang Zhang, Yang Gao, Sathishkumar V E
2022 PLoS ONE  
However, the existing detection methods fail to take full advantage of the semantics of the microblog information propagation graph.  ...  is a graph-based rumor detection model, to capture and aggregate the semantic information using attention layers.  ...  This is because the PPC model combines variations from different levels after the propagation path is learned by the RNN and CNN, which is remarkably beneficial to rumor detection.  ... 
doi:10.1371/journal.pone.0266598 pmid:35413070 pmcid:PMC9004763 fatcat:wcuiz6dqpjhxtijsphw7ngk3ma

Rumor Detection with Self-supervised Learning on Texts and Social Graph [article]

Yuan Gao, Xiang Wang, Xiangnan He, Huamin Feng, Yongdong Zhang
2022 arXiv   pre-print
Rumor detection has become an emerging and active research field in recent years.  ...  We term this framework as Self-supervised Rumor Detection (SRD). Extensive experiments on three real-world datasets validate the effectiveness of SRD for automatic rumor detection on social media.  ...  Graph Convolutional Network Inspired by the great success of Convolutional Neural Network (CNN), Graph Neural Networks (GNNs) begin to emerge in supervised or semi-supervised tasks like node classification  ... 
arXiv:2204.08838v1 fatcat:ybmyd4ipxfh3zamwxcd53ha7k4

Modeling microscopic and macroscopic information diffusion for rumor detection

Xueqin Chen, Fan Zhou, Fengli Zhang, Marcello Bonsangue
2021 International Journal of Intelligent Systems  
Recently, deep learning solutions have emerged as the de facto methods which detect online rumors in an end-to-end manner.  ...  It leverages graph neural networks to learn the macroscopic diffusion of rumor propagation and capture microscopic diffusion patterns using bidirectional recurrent neural networks while taking into account  ...  ACKNOWLEDGMENTS This study was supported by National Natural Science Foundation of China (Grant no. 62072077), Sichuan Regional Innovation Cooperation Project (Grant no. 2020YFQ0018), and National Key  ... 
doi:10.1002/int.22518 fatcat:wfzd5mkynvbqxo32q2kojkh6oq

Attention Based Neural Architecture for Rumor Detection with Author Context Awareness [article]

Sansiri Tarnpradab, Kien A. Hua
2019 arXiv   pre-print
In this research, we propose an ensemble neural architecture to detect rumor on Twitter.  ...  whether the shared content is rumor or legitimate news.  ...  The topic of rumor detection has gained much interest over the years since it helps prevent problems arising after the rumor has emerged.  ... 
arXiv:1910.01458v1 fatcat:dsickjmnc5cprogkfw4yrcjtkq

Rumor Detection on Social Media with Graph Structured Adversarial Learning

Xiaoyu Yang, Yuefei Lyu, Tian Tian, Yifei Liu, Yudong Liu, Xi Zhang
2020 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  
In addition to text information, recent detection methods began to exploit the graph structure in the propagation network.  ...  However, without a rigorous design, rumors may evade such graph models using various camouflage strategies by perturbing the structured data.  ...  We focus on the attack type that fools the graph neural network-based detection model by manipulating the graph structure.  ... 
doi:10.24963/ijcai.2020/197 dblp:conf/ijcai/YangLTLLZ20 fatcat:p7tnn3mkkfby3oc4c5dowyplta

Adapting Pre-trained Language Models to Rumor Detection on Twitter

Hamda Slimi, Ibrahim Bounhas, Yahya Slimani
2021 Journal of universal computer science (Online)  
In this paper, we propose an approach that seeks to detect emerging and unseen rumors on Twitter by adapting a pre-trained language model to the task of rumor detection, namely RoBERTa.  ...  These circumstances required the development of solutions to monitor and detect rumor in a timely manner.  ...  By evaluating their approach on various datasets they have proven the ability of their model to detect unseen emerging rumors.  ... 
doi:10.3897/jucs.65918 fatcat:bdtfurxsfjastmqsrffdvigafm

Rumor Detection on Social Media: Datasets, Methods and Opportunities [article]

Quanzhi Li, Qiong Zhang, Luo Si, Yingchi Liu
2019 arXiv   pre-print
Many efforts have been taken to detect and debunk rumors on social media by analyzing their content and social context using machine learning techniques.  ...  This paper gives an overview of the recent studies in the rumor detection field.  ...  Liu and Wu (2018) construct user representations using network embedding approaches on the social network graph.  ... 
arXiv:1911.07199v1 fatcat:h4fk3dyodjgyvffwuo6q5d2tnm

GCNRDM: A Social Network Rumor Detection Method Based on Graph Convolutional Network in Mobile Computing

Dawei Xu, Qing Liu, Liehuang Zhu, Zhonghua Tan, Feng Gao, Jian Zhao, Lihua Yin
2021 Wireless Communications and Mobile Computing  
The innovation of the paper proposes a rumor detection model based on the graph convolutional network, which lies in considering the propagation structure among users. It has a strong practical value.  ...  We use a high-order graph neural network (K-GNN) to extract the rumor posting features.  ...  [14] constructed a rumor propagation tree kernel to detect rumors by evaluating the similarity between rumor propagation tree structures.  ... 
doi:10.1155/2021/1690669 fatcat:yu4ks7t2xzftfedwcwiveomgmu

The Future of Misinformation Detection: New Perspectives and Trends [article]

Bin Guo, Yasan Ding, Lina Yao, Yunji Liang, Zhiwen Yu
2019 arXiv   pre-print
We first give a brief review of the literature history of MID, based on which we present several new research challenges and techniques of it, including early detection, detection by multimodal data fusion  ...  , and explanatory detection.  ...  [114] also explore whether the detection of emerging rumors could be benefited by the knowledge acquired from historical crowdsourced data. ey observe that similar rumors o en lead to similar behavior  ... 
arXiv:1909.03654v1 fatcat:34h2os2pzrbm3kqluk5uajtr6i
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