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

Tra My Nguyen; Hong-Woo Chun; Myunggwon Hwang; Lee-Nam Kwon; Jae-Min Lee; Kanghee Park; Jason J Jung

doi:10.7717/peerj-cs.1277

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

PeerJ Comput Sci. 2023 Mar 20:9:e1277. doi: 10.7717/peerj-cs.1277. eCollection 2023.

Authors

Tra My Nguyen¹, Hong-Woo Chun², Myunggwon Hwang³, Lee-Nam Kwon², Jae-Min Lee², Kanghee Park², Jason J Jung¹

Affiliations

¹ Department of Computer Engineering, Chung-Ang University, Seoul, Korea.
² Korea Institute of Science and Technology Information, Seoul, Korea.
³ Korea Institute of Science and Technology Information, Daejeon, Korea.

Abstract

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.

Keywords: Event detection and exploration; Knowledge graph; SocioPedia; Visual analytics.

Grants and funding

This research was supported by the Chung-Ang University Young Scientist Scholarship in 2021. This work was also supported by the Ministry of Science and ICT(MSIT) grant by the Korean government. (KISTI Project No. K-22-L03-C02). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.