Burst Topic Detection in Real Time Spatial–Temporal Data Stream
In the field of social network, fast detection of the burst topic plays a decisive role in emergency response and disposal. However, social data are noisy and sparse, which evolves with time going on and space changing make it difficult to catch the instant semantics with traditional methods. Instea...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8741054/ |
| _version_ | 1818935922868092928 |
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| author | Chuangying Zhu Junping Du Qiang Zhang Ziwen Zhu Lei Shi |
| author_facet | Chuangying Zhu Junping Du Qiang Zhang Ziwen Zhu Lei Shi |
| author_sort | Chuangying Zhu |
| collection | DOAJ |
| description | In the field of social network, fast detection of the burst topic plays a decisive role in emergency response and disposal. However, social data are noisy and sparse, which evolves with time going on and space changing make it difficult to catch the instant semantics with traditional methods. Instead of passively waiting for an emergency topic, we try to detect the latent burst topic in its budding stage. In this paper, we propose a fast burst topic detect method, namely, FBTD, which aligns data prediction with characteristic calculation to detect burst term from the real-time spatial-temporal data stream and integrates local topic detection with global topic detection to find the spatial-temporal burst topic. Our method controls the delay within a 0.1 s level while preserving the topic quality. The experiments show that preferable effects are procured, and our method outperforms the state-of-the-art approaches in terms of effectiveness. |
| first_indexed | 2024-12-20T05:27:52Z |
| format | Article |
| id | doaj.art-74eb4832f1594ef3a7b7ab9a7f747f8f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T05:27:52Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-74eb4832f1594ef3a7b7ab9a7f747f8f2022-12-21T19:51:50ZengIEEEIEEE Access2169-35362019-01-017827098272010.1109/ACCESS.2019.29236828741054Burst Topic Detection in Real Time Spatial–Temporal Data StreamChuangying Zhu0https://orcid.org/0000-0002-7055-0716Junping Du1Qiang Zhang2Ziwen Zhu3Lei Shi4Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, ChinaBeijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, ChinaBeijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, ChinaSchool of Economics, Huazhong University of Science and Technology, Wuhan, ChinaBeijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, ChinaIn the field of social network, fast detection of the burst topic plays a decisive role in emergency response and disposal. However, social data are noisy and sparse, which evolves with time going on and space changing make it difficult to catch the instant semantics with traditional methods. Instead of passively waiting for an emergency topic, we try to detect the latent burst topic in its budding stage. In this paper, we propose a fast burst topic detect method, namely, FBTD, which aligns data prediction with characteristic calculation to detect burst term from the real-time spatial-temporal data stream and integrates local topic detection with global topic detection to find the spatial-temporal burst topic. Our method controls the delay within a 0.1 s level while preserving the topic quality. The experiments show that preferable effects are procured, and our method outperforms the state-of-the-art approaches in terms of effectiveness.https://ieeexplore.ieee.org/document/8741054/Burst topicdata predictiondata streamreal-timesocial network |
| spellingShingle | Chuangying Zhu Junping Du Qiang Zhang Ziwen Zhu Lei Shi Burst Topic Detection in Real Time Spatial–Temporal Data Stream IEEE Access Burst topic data prediction data stream real-time social network |
| title | Burst Topic Detection in Real Time Spatial–Temporal Data Stream |
| title_full | Burst Topic Detection in Real Time Spatial–Temporal Data Stream |
| title_fullStr | Burst Topic Detection in Real Time Spatial–Temporal Data Stream |
| title_full_unstemmed | Burst Topic Detection in Real Time Spatial–Temporal Data Stream |
| title_short | Burst Topic Detection in Real Time Spatial–Temporal Data Stream |
| title_sort | burst topic detection in real time spatial x2013 temporal data stream |
| topic | Burst topic data prediction data stream real-time social network |
| url | https://ieeexplore.ieee.org/document/8741054/ |
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