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Rumour Detection based on Graph Convolutional Neural Net

Na Bai, Fanrong Meng, Xiaobin Rui, Zhixiao Wang
2021 IEEE Access  
Based on SR-graphs, we propose an Ensemble Graph Convolutional Neural Net with a Nodes Proportion Allocation Mechanism (EGCN) for the rumor detection task.  ...  Rumor detection is an important research topic in social networks, and lots of rumor detection models are proposed in recent years.  ...  We stack multiple graph convolution layers as follows: Z t+1 = f D −1Ã Z t W t (6) After several spatial graph convolution layers, a SortPooling layer [21] is used to sort the feature descriptors, each  ... 
doi:10.1109/access.2021.3050563 fatcat:7g2bmqf7uff3znbmsywudzb3r4

Jointly embedding the local and global relations of heterogeneous graph for rumor detection [article]

Chunyuan Yuan, Qianwen Ma, Wei Zhou, Jizhong Han, Songlin Hu
2019 arXiv   pre-print
Then, we model the global relationships among all source tweets, retweets, and users as a heterogeneous graph to capture the rich structural information for rumor detection.  ...  In this paper, we present a novel global-local attention network (GLAN) for rumor detection, which jointly encodes the local semantic and global structural information.  ...  Moreover, we conduct another experiment with the combination of multiple different convolutional kernel sizes.  ... 
arXiv:1909.04465v2 fatcat:meju6xiptbeehaiu7qf6emdspa

Towards Propagation Uncertainty: Edge-enhanced Bayesian Graph Convolutional Networks for Rumor Detection [article]

Lingwei Wei, Dou Hu, Wei Zhou, Zhaojuan Yue, Songlin Hu
2021 arXiv   pre-print
Specifically, we propose a novel Edge-enhanced Bayesian Graph Convolutional Network (EBGCN) to capture robust structural features.  ...  Detecting rumors on social media is a very critical task with significant implications to the economy, public health, etc.  ...  The Proposed Model In this section, we propose a novel edge-enhanced bayesian graph convolutional network (EBGCN) for rumor detection in Section 4.2.  ... 
arXiv:2107.11934v1 fatcat:bosu37bur5dtrctae5y4sa233m

Rumor Detection on Social Media via Fused Semantic Information and a Propagation Heterogeneous Graph

Zunwang Ke, Zhe Li, Chenzhi Zhou, Jiabao Sheng, Wushour Silamu, Qinglang Guo
2020 Symmetry  
An organic combination of text semantics and propagating heterogeneous graphs is then used to train a rumor detection classifier.  ...  To this end, we propose KZWANG, a framework for rumor detection that provides sufficient domain knowledge to classify rumors accurately, and semantic information and a propagation heterogeneous graph are  ...  • We concatenate the source microblog features with other microblogs at each graph convolutional layer to comprehensively use the root feature information and obtain excellent rumor detection performance  ... 
doi:10.3390/sym12111806 fatcat:ahelbdk7qrel5e54ifokqkrpma

Deep Feature Fusion for Rumor Detection on Twitter

Zhirui Luo, Qingqing Li, Jun Zheng
2021 IEEE Access  
Twitter rumor detection.  ...  A convolutional neural network (CNN) architecture is then developed to extract temporal-structural features from the encoded propagation tree.  ...  graph of multiple propagation trees [13] .  ... 
doi:10.1109/access.2021.3111790 fatcat:zl7dsj5hpve7jl77ukvdlhfmnq

Birds of a Feather Rumor Together? Exploring Homogeneity and Conversation Structure in Social Media for Rumor Detection

Jiawen Li, Shiwen Ni, Hung-Yu Kao
2020 IEEE Access  
[28] proposed a propagation-based Fake News Detection by convolutional graph networks. [29] detected rumors by using Multi-modal Social Graph.  ...  [18] , [19] combined text and RNN or convolutional neural network (CNN) for rumor detection. [20] combined the attention mechanism with the text to detect rumors at an early age.  ...  His research interests include Web information retrieval / extraction, search engine, knowledge management, data mining, social network analysis and bioinformatics.  ... 
doi:10.1109/access.2020.3040263 fatcat:h25uaoyhynh7jgdzf3hyljcc7i

Dual Co-Attention-Based Multi-Feature Fusion Method for Rumor Detection

Changsong Bing, Yirong Wu, Fangmin Dong, Shouzhi Xu, Xiaodi Liu, Shuifa Sun
2022 Information  
The proposed BERT-based Dual Co-attention Neural Network (BDCoNN) method for rumor detection, which uses BERT for word embedding .  ...  In the BDCoNN method, user discrete features and identity descriptors in user profiles are extracted using a one-dimensional convolutional neural network (CNN) and TextCNN, respectively.  ...  [25] introduced a graph convolutional network (GCN) to explore the mechanisms of top-down and bottom-up rumor propagation. Ma et al.  ... 
doi:10.3390/info13010025 fatcat:svzkriucofg27kb6zgegdslpby

Guest Editorial Introduction to the Special Section on Scalability and Privacy in Social Networks

Donghyun Kim, My T. Thai, R. N. Uma
2020 IEEE Transactions on Network Science and Engineering  
scheme with user privacy protection, and a new random matrix-based approach to publish online social network graph with privacy protection.  ...  Therefore, the creation and the distribution of such social network graphs have to be done with great caution.  ... 
doi:10.1109/tnse.2019.2959674 fatcat:pm5hzj4pczddhmjlucpxhk75ey

An End-to-End Rumor Detection Model Based on Feature Aggregation

Aoshuang Ye, Lina Wang, Run Wang, Wenqi Wang, Jianpeng Ke, Danlei Wang, Hocine Cherifi
2021 Complexity  
It is crucial to identify rumors automatically. Machine learning technology is widely implemented in the identification and detection of misinformation on social networks.  ...  The social network has become the primary medium of rumor propagation. Moreover, manual identification of rumors is extremely time-consuming and laborious.  ...  [19] explored both propagation and dispersion features of rumors with bidirectional graph convolutional networks (Bi-GCNs). Kwon et al.  ... 
doi:10.1155/2021/6659430 fatcat:6ijc5znemja33oepr5hqavhe6y

An Adaptive Deep Transfer Learning Model for Rumor Detection without Sufficient Identified Rumors

Meicheng Guo, Zhiwei Xu, Limin Liu, Mengjie Guo, Yujun Zhang
2020 Mathematical Problems in Engineering  
With the extensive usage of social media platforms, spam information, especially rumors, has become a serious problem of social network platforms.  ...  Experiments based on real-world datasets demonstrate that the proposed model achieves more accurate rumor detection and significantly outperforms state-of-the-art rumor detection models.  ...  Conclusion In this paper, we present an effective deep transfer model based on convolutional neural network, TL-CNN, to detect rumors with limited amount of training data.  ... 
doi:10.1155/2020/7562567 fatcat:hnt5rciirjcivlnwfhwnv3qitq

Identifying Possible Rumor Spreaders on Twitter: A Weak Supervised Learning Approach [article]

Shakshi Sharma, Rajesh Sharma
2021 arXiv   pre-print
In particular, to exploit the inherent network property in this dataset (user-user reply graph), we explore Graph Convolutional Network (GCN), a type of Graph Neural Network (GNN) technique.  ...  We compare GCN results with the other approaches: SVM, RF, and LSTM.  ...  Some of the papers [25] - [27] have employed Graph Convolutional Network (GCN) based approaches for detecting rumors. Unlike these works, we study the rumors to identify possible rumor spreaders.  ... 
arXiv:2010.07647v2 fatcat:6b5mw47cprehnhqynd4xh3kaem

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  
learning with propagation graph neural network).  ...  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  ...  On the basis of PGNN, we propose two rumor detection models, GLO-PGNN (rumor detection model based on the global embedding with propagation graph neural network) and ENS-PGNN (rumor detection model based  ... 
doi:10.1016/j.eswa.2020.113595 pmid:32565619 pmcid:PMC7274137 fatcat:pkowkwq5anckfdhc3guh6jer4e

SRLF: A Stance-aware Reinforcement Learning Framework for Content-based Rumor Detection on Social Media [article]

Chunyuan Yuan, Wanhui Qian, Qianwen Ma, Wei Zhou, Songlin Hu
2021 arXiv   pre-print
Recent studies combine the stances of users' comments with news content to capture the difference between true and false rumors.  ...  Although the user's stance is effective for rumor detection, the manual labeling process is time-consuming and labor-intensive, which limits the application of utilizing it to facilitate rumor detection  ...  Recurrent neural network (RNN) [17] , convolutional neural network (CNN) [18] and graph neural network [9] have been imported to learn the representations from news content or diffusion graph.  ... 
arXiv:2105.04098v1 fatcat:qvtvru4qyjax7kk7p5cphnp6iy

DEAP-FAKED: Knowledge Graph based Approach for Fake News Detection [article]

Mohit Mayank, Shakshi Sharma, Rajesh Sharma
2021 arXiv   pre-print
The approaches span from exploiting techniques related to network analysis, Natural Language Processing (NLP), and the usage of Graph Neural Networks (GNNs).  ...  In this work, we propose DEAP-FAKED, a knowleDgE grAPh FAKe nEws Detection framework for identifying Fake News.  ...  utilizing Graph Convolutional Network [15] , gated GNN [20] .  ... 
arXiv:2107.10648v1 fatcat:tmgvnmpbr5aipnt36wqqmww5qe

Fake News Detection via Knowledge-driven Multimodal Graph Convolutional Networks

Youze Wang, Shengsheng Qian, Jun Hu, Quan Fang, Changsheng Xu
2020 Proceedings of the 2020 International Conference on Multimedia Retrieval  
In recent years, there are more and more models with deep neural networks to learn feature representations from multiple aspects.  ...  • CNN [32] : CNN uses a convolution network to learn rumor representations by framing the relevant posts as fixed-length sequence.• TextGCN [31] : Text Graph Convolution Network(TextGCN)is an algorithm  ... 
doi:10.1145/3372278.3390713 dblp:conf/mir/WangQHFX20 fatcat:bdtdwo3pwbhm5bbipy7w3x6idi
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