Adversarial Active Learning Based Heterogeneous Graph Neural Network for Fake News Detection

Ren, Yuxiang; Wang, Bo; Zhang, Jiawei; Chang, Yi

doi:10.1109/icdm50108.2020.00054

Cited by 48 publications

(17 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Graph neural networks have been studied to detect anomalies in various domains, such as detecting review spams in business websites [10], [26], [28], [31], rumors in social media [5], [52], [55], fake news [33], [37], financial fraud [29], [30], [46], insurance fraud [27], and bot fraud [57]. Most of them target how to design a proper aggregator that can distinguish the effects of different neighbors and reduce the inconsistency issue [31] during message passing.…”

Section: Graph-based Anomaly Detectionmentioning

confidence: 99%

GCCAD: Graph Contrastive Coding for Anomaly Detection

Chen¹,

Zhang²,

Zhang³

et al. 2021

Preprint

View full text Add to dashboard Cite

Graph-based anomaly detection has been widely used for detecting malicious activities in real-world applications. Existing attempts to address this problem have thus far focused on structural feature engineering or learning in the binary classification regime. In this work, we propose to leverage graph contrastive coding and present the supervised GCCAD model for contrasting abnormal nodes with normal ones in terms of their distances to the global context (e.g., the average of all nodes). To handle scenarios with scarce labels, we further enable GCCAD as a self-supervised framework by designing a graph corrupting strategy for generating synthetic node labels. To achieve the contrastive objective, we design a graph neural network encoder that can infer and further remove suspicious links during message passing, as well as learn the global context of the input graph. We conduct extensive experiments on four public datasets, demonstrating that 1) GCCAD significantly and consistently outperforms various advanced baselines and 2) its self-supervised version without fine-tuning can achieve comparable performance with its fully supervised version.

show abstract

Section: Graph-based Anomaly Detectionmentioning

confidence: 99%

GCCAD: Graph Contrastive Coding for Anomaly Detection

Chen¹,

Zhang²,

Zhang³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…News articles and their metadata are being used to build heterogeneous information networks [18]. Heterogeneous Graph Neural Networks such as Adversarial active learning [25] and Graph-aware co-attention networks [26] have also been explored for detecting Fake News. As part of the heterogeneous graphs, KGs-based approaches have also been investigated in Fake News detection.…”

Section: Related Workmentioning

confidence: 99%

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

Mayank¹,

Sharma²,

Sharma³

2021

Preprint

View full text Add to dashboard Cite

Fake News on social media platforms has attracted a lot of attention in recent times, primarily for events related to politics (2016 US Presidential elections), healthcare (infodemic during COVID-19), to name a few. Various methods have been proposed for detecting Fake News. 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. Our approach is a combination of the NLP -where we encode the news content, and the GNN technique -where we encode the Knowledge Graph (KG). A variety of these encodings provides a complementary advantage to our detector. We evaluate our framework using two publicly available datasets containing articles from domains such as politics, business, technology, and healthcare. As part of dataset pre-processing, we also remove the bias, such as the source of the articles, which could impact the performance of the models. DEAP-FAKED obtains an F1-score of 88% and 78% for the two datasets, which is an improvement of ∼21%, and ∼3% respectively, which shows the effectiveness of the approach.

show abstract

“…Besides the application in fake news detection [22], financial assessment [23], many researchers also use GNNs for review spam problem. For example, Wang et al [24] trains a GCN [19] model to find fraudsters.…”

Section: B Graph Neural Networkmentioning

confidence: 99%

Fake Reviewer Group Detection in Online Review Systems

Chen¹,

Li²,

Yu³

et al. 2021

Preprint

View full text Add to dashboard Cite

Online review systems are important components in influencing customers' purchase decisions. To manipulate a product's reputation, many stores hire large numbers of people to produce fake reviews to mislead customers. Previous methods tackle this problem by detecting malicious individuals, ignoring the fact that the spam activities are often formed in groups, where individuals work collectively to write fake reviews. Fake reviewer group detection, however, is more challenging due to the difficulties in capturing the underlying relationships in groups. In this work, we present an unsupervised and end-toend approach for fake reviewer group detection in online reviews. Specifically, our method can be summarized into two procedures. First, cohensive groups are detected with modularity-based graph convolutional networks. Then the suspiciousness of each group is measured by several anomaly indicators from both individual and group levels. The fake reviewer groups can be finally detected through suspiciousness. Extensive experiments are conducted on real-world datasets, and the results show that our proposed method is effective in detecting fake reviewer groups compared with the state-of-the-art baselines.

show abstract

Adversarial Active Learning Based Heterogeneous Graph Neural Network for Fake News Detection

Cited by 48 publications

References 28 publications

GCCAD: Graph Contrastive Coding for Anomaly Detection

GCCAD: Graph Contrastive Coding for Anomaly Detection

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

Fake Reviewer Group Detection in Online Review Systems

Contact Info

Product

Resources

About