DETERRENT: Knowledge Guided Graph Attention Network for Detecting Healthcare Misinformation

Cui, Limeng; Seo, Haeseung; Tabar, Maryam; Ma, Fenglong; Wang, Suhang; Lee, Dongwon

doi:10.1145/3394486.3403092

Cited by 115 publications

(65 citation statements)

References 28 publications

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“…Efforts have been made to address the explainable rumor detection problem in many communities such as data mining, machine learning and computer vision [3], [19], [21], [24], [25], [26], [27]. For example, Yang et al [24] proposed a multi-level attention Recurrent Neural Network (RNN) to detect false documents and retrieve related sentences or words from the documents with attention scores as explanations.…”

Section: Explainable Rumor Detectionmentioning

confidence: 99%

What and Why? Towards Duo Explainable Fauxtography Detection Under Constrained Supervision

Kou

Zhang

Shang

et al. 2023

IEEE Trans. Big Data

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Section: Explainable Rumor Detectionmentioning

confidence: 99%

What and Why? Towards Duo Explainable Fauxtography Detection Under Constrained Supervision

Kou

Zhang

Shang

et al. 2023

IEEE Trans. Big Data

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“…Researchers start focusing on heterogeneous graphs, as they contain multiple types of nodes and links to represent different entities and relations, which mimic the data flows more closely in the real-world network. 74,[145][146][147][148] For instance, under problem formulation in Zhong et al, a node can be a customer, a merchant, or a device. 72 In the graph constructed in Hu et al, an edge implies social connection, money transaction, or device ownership, and so forth.…”

Section: Review Llmentioning

confidence: 99%

Intelligent financial fraud detection practices in post-pandemic era

Zhu

Qin

et al. 2021

The Innovation

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The great losses caused by financial fraud have attracted continuous attention from academia, industry, and regulatory agencies. More concerning, the ongoing coronavirus pandemic (COVID-19) unexpectedly shocks the global financial system and accelerates the use of digital financial services, which brings new challenges in effective financial fraud detection. This paper provides a comprehensive overview of intelligent financial fraud detection practices. We analyze the new features of fraud risk caused by the pandemic and review the development of data types used in fraud detection practices from quantitative tabular data to various unstructured data. The evolution of methods in financial fraud detection is summarized, and the emerging Graph Neural Network methods in the post-pandemic era are discussed in particular. Finally, some of the key challenges and potential directions are proposed to provide inspiring information on intelligent financial fraud detection in the future.

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“…A few studies use the external knowledge to supplement post contents to obtain better representations for rumor detection. A knowledge-guided article embedding is learned for healthcare misinformation detection by incorporating medical knowledge graph and propagating the node embeddings through knowledge paths (Cui et al, 2020). The multimodal knowledge-aware representation and eventinvariant features are learned together to form the event representation in Zhang et al (2019), which is fed into a deep neural network for rumor detection.…”

Section: Related Workmentioning

confidence: 99%

Inconsistency Matters: A Knowledge-guided Dual-inconsistency Network for Multi-modal Rumor Detection

Sun¹,

Zhang²,

Ma³

et al. 2021

Findings of the Association for Computational Linguistics: EMNLP 2021

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Rumor spreaders are increasingly utilizing multimedia content to attract the attention and trust of news consumers. Though a set of rumor detection models have exploited the multimodal data, they seldom consider the inconsistent relationships among images and texts. Moreover, they also fail to find a powerful way to spot the inconsistency information among the post contents and background knowledge. Motivated by the intuition that rumors are more likely to have inconsistency information in semantics, a novel Knowledge-guided Dualinconsistency network is proposed to detect rumors with multimedia contents. It can capture the inconsistent semantics at the cross-modal level and the content-knowledge level in one unified framework. Extensive experiments on two public real-world datasets demonstrate that our proposal can outperform the state-ofthe-art baselines.

show abstract

DETERRENT: Knowledge Guided Graph Attention Network for Detecting Healthcare Misinformation

Cited by 115 publications

References 28 publications

What and Why? Towards Duo Explainable Fauxtography Detection Under Constrained Supervision

What and Why? Towards Duo Explainable Fauxtography Detection Under Constrained Supervision

Intelligent financial fraud detection practices in post-pandemic era

Inconsistency Matters: A Knowledge-guided Dual-inconsistency Network for Multi-modal Rumor Detection

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