Nguyen Tra My, Chun Hong-Woo, Hwang Myunggwon, Kwon Lee-Nam, Lee Jae-Min, Park Kanghee, Jung Jason J
Department of Computer Engineering, Chung-Ang University, Seoul, Korea.
Korea Institute of Science and Technology Information, Seoul, Korea.
PeerJ Comput Sci. 2023 Mar 20;9:e1277. doi: 10.7717/peerj-cs.1277. eCollection 2023.
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.
在当今信息爆炸的时代,从社交网络中挖掘事件对于许多应用来说已成为一项至关重要的任务。为了获得关于社会事件有价值的全面而深入的见解,可视化分析(VA)系统已被广泛用作一种有前景的解决方案。然而,由于海量的社会数据具有高度的多样性和复杂性,传统实时可视化分析系统中能够实现的事件挖掘任务数量有限。在本文中,我们介绍了SocioPedia+,这是一个用于在时空领域进行社会事件挖掘的实时可视化分析系统。通过将社会知识图谱分析维度引入系统多变量分析中,SocioPedia+中的事件挖掘过程可以得到显著增强,从而使系统能够执行可视化分析和社会事件挖掘的全部所需任务。此外,SocioPedia+已针对从宏观(事件层面)到微观(知识层面)的不同层次的事件分析可视化进行了优化。然后通过一个详细的案例研究对该系统进行实现和研究,以评估其在事件挖掘应用中的实用性和可视化有效性。