Dynamic graph neural network for fake news detection

Song, Chenguang; Teng, Yiyang; Zhu, Yangfu; Wei, Siqi; Wu, Bin

doi:10.1016/j.neucom.2022.07.057

Cited by 26 publications

(16 citation statements)

References 27 publications

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“…Dynamic-GCN [38] and Dynamic-GNN [39] capture temporal dynamics by taking snapshots of the propagation graph at different time points. These graph snapshots are encoded by a GNN (e.g., a GCN) to generate graph representations, which are then processed using sequence modeling approaches, such as self-attention encoders.…”

Section: B Dynamic Graph-based Methodsmentioning

confidence: 99%

“…RNLNP [37] claim, comments responsive propagation GCN Introduce linear propagation information in addition to non-linear graph modelling. Dynamic-GCN/GNN [38], [39] claim, comments responsive propagation GCN Take snapshots of the propagation graph to capture temporal information. TGNF [40] claim, comments responsive propagation TGN [41] Use advanced temporal graph model to capture temporal information of the propagation graph evolution process.…”

Section: Gcnmentioning

confidence: 99%

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Heterogeneous graph neural networks with denoising for graph embeddings

Dong

Zhang

Pang

et al. 2022

Knowledge-Based Systems

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Section: B Dynamic Graph-based Methodsmentioning

confidence: 99%

Section: Gcnmentioning

confidence: 99%

Heterogeneous graph neural networks with denoising for graph embeddings

Dong

Zhang

Pang

et al. 2022

Knowledge-Based Systems

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“…It is able to accurately model the temporal and structural details of rumor propagation. Song et al (2021bSong et al ( , 2022 proposed modeling rumor propagation patterns using dynamic graphs, using GCN to encode structural information, gating networks to encode temporal information, and average pooling of individual node embeddings to produce the full graph representation. Sun et al (2022) leverage external knowledge to improve the model's comprehension of the text, while the way of encoding spatial and temporal information is similar with the previous methods.…”

Section: Propagation-based Rumor Detectionmentioning

confidence: 99%

“…Similar to previous study (Song et al, 2022;Sun et al, 2022), the source tweet, retweets, and replies are considered nodes. We consider retweet or respond behaviors as edges.…”

Section: Datasetsmentioning

confidence: 99%

“…It encodes rumor propagation networks for the rumor detection using bottom-up and top-down GCN. • DGNF (Song et al, 2022): An approach for modeling rumor propagation networks using dynamic graphs. It employs GAT to represent the structural information about the rumor propagation and a self-attentive method to simulate temporal information.…”

Section: Baseline Approachesmentioning

confidence: 99%

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DGTR: Dynamic graph transformer for rumor detection

Wei

Xiang

et al. 2023

Front. Res. Metr. Anal.

Self Cite

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Social media rumors have the capacity to harm the public perception and the social progress. The news propagation pattern is a key clue for detecting rumors. Existing propagation-based rumor detection methods represent propagation patterns as a static graph structure. They simply consider the structure information of news distribution in social networks and disregard the temporal information. The dynamic graph is an effective modeling tool for both the structural and temporal information involved in the process of news dissemination. Existing dynamic graph representation learning approaches struggle to capture the long-range dependence of the structure and temporal sequence as well as the rich semantic association between full graph features and individual parts. We build a transformer-based dynamic graph representation learning approach for rumor identification DGTR to address the aforementioned challenges. We design a position embedding format for the graph data such that the original transformer model can be utilized for learning dynamic graph representations. The model can describe the structural long-range reliance between the dynamic graph nodes and the temporal long-range dependence between the temporal snapshots by employing a self-attention mechanism. In addition, the CLS token in transformer may model the rich semantic relationships between the complete graph and each subpart. Extensive experiments demonstrate the superiority of our model when compared to the state of the art.

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Exploring Fake News Detection with Heterogeneous Social Media Context Graphs

Donabauer

Kruschwitz

2023

Lecture Notes in Computer Science

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Pre-training of neural networks has recently revolutionized the field of Natural Language Processing (NLP) and has before demonstrated its effectiveness in computer vision. At the same time, advances around the detection of fake news were mainly driven by the context-based paradigm, where different types of signals (e.g. from social media) form graph-like structures that hold contextual information apart from the news article to classify. We propose to merge these two developments by applying pretraining of Graph Neural Networks (GNNs) in the domain of context-based fake news detection. Our experiments provide an evaluation of different pre-training strategies for graph-based misinformation detection and demonstrate that transfer learning does currently not lead to significant improvements over training a model from scratch in the domain. We argue that a major current issue is the lack of suitable large-scale resources that can be used for pre-training.

show abstract

Dynamic graph neural network for fake news detection

Cited by 26 publications

References 27 publications

Heterogeneous graph neural networks with denoising for graph embeddings

Heterogeneous graph neural networks with denoising for graph embeddings

DGTR: Dynamic graph transformer for rumor detection

Exploring Fake News Detection with Heterogeneous Social Media Context Graphs

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