SocioPedia+: a visual analytics system for social knowledge graph-based event exploration

• Published in: PeerJ. Computer science Vol. 9; p. e1277
• Main Authors: Nguyen, Tra My, Chun, Hong-Woo, Hwang, Myunggwon, Kwon, Lee-Nam, Lee, Jae-Min, Park, Kanghee, Jung, Jason J
• Format: Journal Article
• Language: English
• Published: United States PeerJ Inc 20.03.2023
• Subjects: Artificial Intelligence; Event detection and exploration; Knowledge graph; Social Computing; SocioPedia; Visual Analytics; World Wide Web and Web Science; Visual analytics; SocioPedia; Event detection and exploration; Knowledge graph
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/PEERJ-CS.1277

In the recent era of information explosion, exploring event from social networks has recently been a crucial task for many applications. To derive valuable comprehensive and thorough insights on social events, visual analytics (VA) system have been broadly used as a promising solution. However, due to the enormous social data volume with highly diversity and complexity, the number of event exploration tasks which can be enabled in a conventional real-time visual analytics systems has been limited. In this article, we introduce SocioPedia+, a real-time visual analytics system for social event exploration in time and space domains. By introducing the dimension of social knowledge graph analysis into the system multivariate analysis, the process of event explorations in SocioPedia+ can be significantly enhanced and thus enabling system capability on performing full required tasks of visual analytics and social event explorations. Furthermore, SocioPedia+ has been optimized for visualizing event analysis on different levels from macroscopic (events level) to microscopic (knowledge level). The system is then implemented and investigated with a detailed case study for evaluating its usefulness and visualization effectiveness for the application of event explorations.