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Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks [article]

Hongzhan Lin, Jing Ma, Mingfei Cheng, Zhiwei Yang, Liangliang Chen, Guang Chen
2021 arXiv   pre-print
We then present a Claim-guided Hierarchical Graph Attention Network for rumor classification, which enhances the representation learning for responsive posts considering the entire social contexts and  ...  Rumors are rampant in the era of social media. Conversation structures provide valuable clues to differentiate between real and fake claims.  ...  Claim-guided Hierarchical Graph Attention Networks In this section, we introduce our Claim-guided Hierarchical Graph Attention Networks to embed the undirected interaction graph for rumor detection.  ... 
arXiv:2110.04522v2 fatcat:wiovw44xmvfgrni4bjqt74viqm

A Rumor Detection Method from Social Network Based on Deep Learning in Big Data Environment

Junjie Cen, Yongbo Li, Deepika Koundal
2022 Computational Intelligence and Neuroscience  
Aiming at the lack of feature extraction ability of rumor detection methods based on the deep learning model, this study proposes a rumor detection method based on deep learning in social network big data  ...  between data, and the statistical features are combined with semantic features to expand the feature space in rumor detection and describe the distribution of data in the feature space to a greater extent  ...  [24] proposed a rumor detection model based on hierarchical neural network (HSA-BLSTM) combined with social information.  ... 
doi:10.1155/2022/1354233 pmid:35387254 pmcid:PMC8979708 fatcat:ziqno7ypqbaqrkap7xondcnk2y

Rumor Detection on Twitter Using Multiloss Hierarchical BiLSTM with an Attenuation Factor [article]

Yudianto Sujana, Jiawen Li, Hung-Yu Kao
2020 arXiv   pre-print
Inspired by the hierarchical model and multitask learning, a multiloss hierarchical BiLSTM model with an attenuation factor is proposed in this paper.  ...  Social media platforms such as Twitter have become a breeding ground for unverified information or rumors.  ...  Zhang et al. (2018) proposed a heterogeneous network for early rumor detection that reached 61% precision. T. Chen et al. (2018) used an RNN network with soft-attention structures. L.  ... 
arXiv:2011.00259v2 fatcat:ageke6hrinhbzgeor5md55jas4

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 .  ...  Its hierarchical attention network structure assigns different attentions to words and sentences.  ... 
doi:10.3390/info13010025 fatcat:svzkriucofg27kb6zgegdslpby

Rumor Detection on Social Media with Hierarchical Adversarial Training [article]

Shiwen Ni, Jiawen Li, Hung-Yu Kao
2021 arXiv   pre-print
The proliferation of rumors on social media has a huge impact on society.  ...  As such, the robustness and generalization of the current rumor detection model are put into question.  ...  Related Work Rumor Detection Scientists around the world have been paying more attention to rumor detection in recent years.  ... 
arXiv:2110.00425v1 fatcat:anpoy7lho5hidlqgzlfhhaktzi

Interpretable Rumor Detection in Microblogs by Attending to User Interactions

Ling Min Serena Khoo, Hai Leong Chieu, Zhong Qian, Jing Jiang
We propose a post-level attention model (PLAN) to model long distance interactions between tweets with the multi-head attention mechanism in a transformer network.  ...  Moreover, the attention mechanism allows us to explain rumor detection predictions at both token-level and post-level.  ...  In this paper, instead of recursive tree models, we propose a transformer network for rumor detection. Rumor Detection In this section, we first define our problem statement.  ... 
doi:10.1609/aaai.v34i05.6405 fatcat:cj4w4dwnsbeczevbsnpmawkuj4

Rumor detection based on graph attention network

Yuwei Lv, Xuemei Sun, Yonggang Wen, Wanru Wang, L. Nguyen
2022 ITM Web of Conferences  
The experimental results show that the proposed method has greatly improved the early detection and accuracy of rumors.  ...  This paper uses graph attention neural network model to learn text features and syntactic relations to solve this problem.  ...  Conclusion In order to improve the accuracy and timeliness of rumor detection, the author proposes a rumor detection method based on a graph attention network model, which uses two attention mechanisms  ... 
doi:10.1051/itmconf/20224702033 fatcat:w7bjyfdnc5fi5lj5xsefwatope

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  ...  With the development of network technology, many social media are flourishing. Due to imperfect Internet regulation, the spread of false rumors has become a common problem on those social platforms.  ...  In this paper, we combine the federated learning framework with a bidirectional graph attention network rumor detection model to construct a federated graph attention network model.  ... 
arXiv:2206.05713v1 fatcat:xzcpzm36f5cp5d57ktyommsmcu

Interpretable Rumor Detection in Microblogs by Attending to User Interactions [article]

Ling Min Serena Khoo, Hai Leong Chieu, Zhong Qian, Jing Jiang
2020 arXiv   pre-print
We propose a post-level attention model (PLAN) to model long distance interactions between tweets with the multi-head attention mechanism in a transformer network.  ...  Moreover, the attention mechanism allows us to explain rumor detection predictions at both token-level and post-level.  ...  Source and Social Network: Another group of work studied the source of fake news, and its social network.  ... 
arXiv:2001.10667v1 fatcat:3vmnyc5afzdxrlub22c4vhzve4

RP-DNN: A Tweet level propagation context based deep neural networks for early rumor detection in Social Media [article]

Jie Gao, Sooji Han, Xingyi Song, Fabio Ciravegna
2020 arXiv   pre-print
Early rumor detection (ERD) on social media platform is very challenging when limited, incomplete and noisy information is available.  ...  Our models achieve state-of-the-art(SoA) performance for detecting unseen rumors on large augmented data which covers more than 12 events and 2,967 rumors.  ...  We employ the idea of hierarchical attention networks (Yang et al., 2016b) and adapt the context-aware model in our networks.  ... 
arXiv:2002.12683v2 fatcat:dx4is5w6yzfk7mcbzf72kxcza4

Automatic Rumor Detection on Microblogs: A Survey [article]

Juan Cao, Junbo Guo, Xirong Li, Zhiwei Jin, Han Guo, Jintao Li
2018 arXiv   pre-print
Many efforts have been taken to defeat online rumors automatically by mining the rich content provided on the open network with machine learning techniques.  ...  We summary the studies of automatic rumor detection so far and present details in three paradigms of rumor detection.  ...  We give a survey of three typical implementations of propagation-based rumor detection approaches, namely user-message-event network, hierarchical content network and conflicting viewpoints network.  ... 
arXiv:1807.03505v1 fatcat:kvwukm7kofhyfd3yjlajagoxce

Explainable Rumor Detection using Inter and Intra-feature Attention Networks [article]

Mingxuan Chen, Ning Wang, K.P. Subbalakshmi
2020 arXiv   pre-print
We tackle the problem of automated detection of rumors in social media in this paper by designing a modular explainable architecture that uses both latent and handcrafted features and can be expanded to  ...  With social media becoming ubiquitous, information consumption from this media has also increased.  ...  are incorporated into the network via attention mechanism to detect rumors.  ... 
arXiv:2007.11057v1 fatcat:mah35jrq6re7vdabskubn57c64

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  ...  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.  ...  Hyperbolic graph neural network [73] could be an ideal one, for its advantages in dealing with hierarchical social 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.  ...  Methods applied in most previous rumor classifiers give an equal weight, or attention, to words in the microblog, and do not take the context beyond microblog contents into account; therefore, the accuracy  ...  With primary focus on the rumor detection domain, our contribution include (i) a new neural architecture to detect rumors on Twitter, (ii) incorporation of word attention, and (iii) incorporation of author  ... 
arXiv:1910.01458v1 fatcat:dsickjmnc5cprogkfw4yrcjtkq

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  
is a graph-based rumor detection model, to capture and aggregate the semantic information using attention layers.  ...  The experimental results on two real-world microblog rumor datasets, Weibo2016 and Weibo2021, demonstrate that the proposed Microblog-HAN can detect microblog rumors with an accuracy of over 92%, demonstrating  ...  Third, a rumor detection model based on a heterogeneous graph attention network with two attention layers is proposed to learn representations of posts using structural and semantic information.  ... 
doi:10.1371/journal.pone.0266598 pmid:35413070 pmcid:PMC9004763 fatcat:wcuiz6dqpjhxtijsphw7ngk3ma
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