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SocioPedia+: a visual analytics system for social knowledge graph-based event exploration
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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280572/ https://www.ncbi.nlm.nih.gov/pubmed/37346548 http://dx.doi.org/10.7717/peerj-cs.1277 |
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author | Nguyen, Tra My Chun, Hong-Woo Hwang, Myunggwon Kwon, Lee-Nam Lee, Jae-Min Park, Kanghee Jung, Jason J. |
author_facet | Nguyen, Tra My Chun, Hong-Woo Hwang, Myunggwon Kwon, Lee-Nam Lee, Jae-Min Park, Kanghee Jung, Jason J. |
author_sort | Nguyen, Tra My |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10280572 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102805722023-06-21 SocioPedia+: a visual analytics system for social knowledge graph-based event exploration Nguyen, Tra My Chun, Hong-Woo Hwang, Myunggwon Kwon, Lee-Nam Lee, Jae-Min Park, Kanghee Jung, Jason J. PeerJ Comput Sci Artificial Intelligence 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. PeerJ Inc. 2023-03-20 /pmc/articles/PMC10280572/ /pubmed/37346548 http://dx.doi.org/10.7717/peerj-cs.1277 Text en ©2023 Nguyen et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Artificial Intelligence Nguyen, Tra My Chun, Hong-Woo Hwang, Myunggwon Kwon, Lee-Nam Lee, Jae-Min Park, Kanghee Jung, Jason J. SocioPedia+: a visual analytics system for social knowledge graph-based event exploration |
title | SocioPedia+: a visual analytics system for social knowledge graph-based event exploration |
title_full | SocioPedia+: a visual analytics system for social knowledge graph-based event exploration |
title_fullStr | SocioPedia+: a visual analytics system for social knowledge graph-based event exploration |
title_full_unstemmed | SocioPedia+: a visual analytics system for social knowledge graph-based event exploration |
title_short | SocioPedia+: a visual analytics system for social knowledge graph-based event exploration |
title_sort | sociopedia+: a visual analytics system for social knowledge graph-based event exploration |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280572/ https://www.ncbi.nlm.nih.gov/pubmed/37346548 http://dx.doi.org/10.7717/peerj-cs.1277 |
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