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Analysis of Techniques for Rumor Detection in Social Media

Ajeet Ram Pathak, Aditee Mahajan, Keshav Singh, Aishwarya Patil, Anusha Nair
2020 Procedia Computer Science  
Moreover, this paper sheds light upon supervised and unsupervised methods and deep learning approaches for rumor detection.  ...  Motivated by the same, this paper focuses on detailed discussion of datasets and state-of-the-art approaches of rumor detection.  ...  A hybrid model of recurrent neural network and convolutional neural networks was designed by Ajao et al.  ... 
doi:10.1016/j.procs.2020.03.281 fatcat:olei4mxf3rbhhgttbzyff4iz7a

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.  ...  [8] proposed an adaptive model and for the first time used the recurrent neural networks to achieve microblog rumor detection.  ... 
doi:10.1155/2020/7562567 fatcat:hnt5rciirjcivlnwfhwnv3qitq

Heterogeneous Graph Convolutional Network-Based Dynamic Rumor Detection on Social Media

Dingguo Yu, Yijie Zhou, Suiyu Zhang, Chang Liu, Keke Shang
2022 Complexity  
Previous methods for rumor detection focused on mining features from content and propagation patterns but neglected the dynamic features with joint content and propagation pattern.  ...  Therefore, detecting rumors from massive information becomes particularly essential.  ...  GRU-RNN [17] : this is a rumor detection method based on recurrent neural networks with GRU units to capture the variation of contextual information of relevant tweet posts over time.  ... 
doi:10.1155/2022/8393736 fatcat:mhsqzw2dqvd7pgmbod6vb2slfm

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  
We use a high-order graph neural network (K-GNN) to extract the rumor posting features.  ...  social network rumor detection based on convolution networks, the use of adjacency matrix between the nodes represent user and the relationship between the constructions of social network topology.  ...  Typical deep learning models include recurrent neural networks (RNN), gated recurrent units (GRU), and recurrent neural networks [3] .  ... 
doi:10.1155/2021/1690669 fatcat:yu4ks7t2xzftfedwcwiveomgmu

Deep learning for misinformation detection on online social networks: a survey and new perspectives

Md Rafiqul Islam, Shaowu Liu, Xianzhi Wang, Guandong Xu
2020 Social Network Analysis and Mining  
The existing studies have mainly focused on three broad categories of misinformation: false information, fake news, and rumor detection.  ...  However, while people enjoy social networks, many deceptive activities such as fake news or rumors can mislead users into believing misinformation.  ...  For example, to identify rumor for events on social media platform, Lin et al. (2019) proposed a novel rumor detection method based on a hierarchical recurrent convolutional neural network.  ... 
doi:10.1007/s13278-020-00696-x pmid:33014173 pmcid:PMC7524036 fatcat:473ziygl7jffbhwvpav3hlmppu

Research status of deep learning methods for rumor detection

Li Tan, Ge Wang, Feiyang Jia, Xiaofeng Lian
2022 Multimedia tools and applications  
To manage the rumors in social media to reduce the harm of rumors in society. Many studies used methods of deep learning to detect rumors in open networks.  ...  And compare the advantages of different methods to detect rumors.  ...  Rumor Detection Based on CNN The convolutional neural network(CNN) is a feedforward neural network that includes convolutional calculations and has a deep structure.  ... 
doi:10.1007/s11042-022-12800-8 pmid:35469150 pmcid:PMC9022167 fatcat:h5vjukpkyzdhnjhikgtpj347e4

Arabic Fake News Detection Using Deep Learning

Khaled M. Fouad, Sahar F. Sabbeh, Walaa Medhat
2022 Computers Materials & Continua  
The identification of fake news and rumors and their dissemination on social media has become a critical requirement.  ...  The deep learning models are used depending on conventional neural nets (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), CNN+LSTM, and CNN + BiLSTM.  ...  In [41] , text, user-based, content-based features, signal features were used for prediction tasks using a hierarchical recurrent convolutional neural network.  ... 
doi:10.32604/cmc.2022.021449 fatcat:3ajnvvsdxrcu3fyt4r2i4fkwmu

A Survey on the Use of Graph Convolutional Networks for Combating Fake News

Iraklis Varlamis, Dimitrios Michail, Foteini Glykou, Panagiotis Tsantilas
2022 Future Internet  
, fake accounts and rumors that spread in social networks.  ...  The role of machine learning techniques, especially neural networks, is crucial in this task.  ...  It also briefly compares GCNs against simple Convolutional Neural Networks and Recurrent Neural Networks.  ... 
doi:10.3390/fi14030070 fatcat:aha4yr6rsjcefhc3cporwtjg7e

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 bidirectional gate recurrent unit network (BiGRU) with a hierarchical attention mechanism is used to learn the hidden layer representation of tweet sequence and comment sequence.  ...  The proposed BERT-based Dual Co-attention Neural Network (BDCoNN) method for rumor detection, which uses BERT for word embedding .  ...  [10] used recurrent neural networks (RNNs) to process text sequence data for rumor detection. Natali et al.  ... 
doi:10.3390/info13010025 fatcat:svzkriucofg27kb6zgegdslpby

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
However, existing works on rumor detection fall short in modeling heterogeneous information, either using one single information source only (e.g. social network, or post content) or ignoring the relations  ...  Rumor detection has become an emerging and active research field in recent years.  ...  Similarly, Liu et al [27] detects fake news using a combination of recurrent and convolutional networks.  ... 
arXiv:2204.08838v1 fatcat:ybmyd4ipxfh3zamwxcd53ha7k4

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.  ...  The experiment on the real-world Twitter dataset collected from two well-known rumor tracking websites demonstrates promising results.  ...  We ensemble Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) to enhance the representation learning.  ... 
arXiv:1910.01458v1 fatcat:dsickjmnc5cprogkfw4yrcjtkq

Hybrid Attention Networks for Chinese Short Text Classification

Yujun Zhou, Jiaming Xu, Jie Cao, Bo Xu, Changliang Li, Bo Xu
2018 Journal of Computacion y Sistemas  
To improve the classification performance for Chinese short text with automatic semantic feature selection, in this paper we propose the Hybrid Attention Networks (HANs) which combines the word-and character-level  ...  In recent years, Artificial Neural Networks (ANNs) also have shown promising results, including Convolutional Neural Networks (CNNs) [7, 8] , Recursive Neural Networks (RecNNs) [15] , Recurrent Neural  ...  Baselines Most neural methods used to text classification are variants of convolutional or recurrent networks.  ... 
doi:10.13053/cys-21-4-2847 fatcat:y3fqhh6oevfg5mvswnzzc44ysu

Federated Graph Attention Network for Rumor Detection [article]

Huidong Wang, Chuanzheng Bai, Jinli Yao
2022 arXiv   pre-print
This paper combines the federated learning paradigm with the bidirectional graph attention network rumor detection model and proposes the federated graph attention network(FedGAT) model for rumor detection  ...  rumor detection.  ...  Zheng, Zhihua, and Renxian (2017) tried to use Convolutional Neural Networks for rumor detection, and constructed the rumor detection framework by combining word vectors with multi-layer convolutions.  ... 
arXiv:2206.05713v1 fatcat:xzcpzm36f5cp5d57ktyommsmcu

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  
However, the previous works have ignored the organic combination of wide dispersion structures in rumor detection and text semantics.  ...  An organic combination of text semantics and propagating heterogeneous graphs is then used to train a rumor detection classifier.  ...  on Twitter. • PPC [29] A novel model for rumor detection by classifying propagation paths by a combination of recurrent and convolutional networks. • GLAN [20] A novel rumor detection model with global-local  ... 
doi:10.3390/sym12111806 fatcat:ahelbdk7qrel5e54ifokqkrpma

Automatic Rumor Detection on Microblogs: A Survey [article]

Juan Cao, Junbo Guo, Xirong Li, Zhiwei Jin, Han Guo, Jintao Li
2018 arXiv   pre-print
Most rumor detection methods can be categorized in three paradigms: the hand-crafted features based classification approaches, the propagation-based approaches and the neural networks approaches.  ...  Many efforts have been taken to defeat online rumors automatically by mining the rich content provided on the open network with machine learning techniques.  ...  RNN-based methods Ma et al. first apply recurrent neural network to detect rumors.  ... 
arXiv:1807.03505v1 fatcat:kvwukm7kofhyfd3yjlajagoxce
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