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Online Opinion Fraud Detection Using Graph Neural Network
Woochang Hyun, Insoo Lee, Bongwon Suh
http://doi.org/10.5626/JOK.2023.50.11.985
This study proposed a graph neural network model to detect opinion frauds that undermine the of information and hinder users" decision-making on online platforms. The proposed method uses methods on a graph of relationships between online reviews to produce relational representations, are then combined with the characteristics of the center nodes to predict fraud. Experimental results on a real-world dataset demonstrate that this approach is more accurate and faster than existing state-of-art methods, while also providing interpretability for key relations. With the help of this study, practitioners will be able to utilize the analytical results in decision-making and overcome the general drawback of neural network-based models" lack of explainability.
Interactive Visual Analytics System for Criminal Intelligence Analysts with Multiple Coordinated Views
Seokweon Jung, Donghwa Shin, Jinwook Bok, Seokhyeon Park, Hyeon Jeon, Jinwook Seo, Insoo Lee, Sooyoung Park
http://doi.org/10.5626/JOK.2023.50.1.47
Data that criminal intelligence analysts have to analyze have become much larger and more complex in recent decades. However, the environment and methods of investigation have not yet kept up with those changes. In this study, we examined current investigation practices in Korean Government Agency. We focused on the sensemaking process of investigation and tried to adopt visual analytics approaches for sensemaking into the investigation. We derived tasks and design requirements and designed a multi-view visual analytics system that could satisfy them. We validated our design with a high-fidelity prototype through a case study to show realistic use cases.
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