ZHU Chuangying, DU Junping, ZHANG Qiang, et al., “FDBST: Fast Discovery of Bursty Spatial-Temporal Topic,” Chinese Journal of Electronics, vol. 29, no. 1, pp. 168-176, 2020, doi: 10.1049/cje.2019.12.002
Citation: ZHU Chuangying, DU Junping, ZHANG Qiang, et al., “FDBST: Fast Discovery of Bursty Spatial-Temporal Topic,” Chinese Journal of Electronics, vol. 29, no. 1, pp. 168-176, 2020, doi: 10.1049/cje.2019.12.002

FDBST: Fast Discovery of Bursty Spatial-Temporal Topic

doi: 10.1049/cje.2019.12.002
Funds:  This work is supported by the National Natural Science Foundation of China (No.61532006, No.61772083, No.61802028) and Science and Technology Major Project of Guangxi (No.GuikeAA18118054).
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  • Corresponding author: DU Junping (corresponding author) was born in Beijing, China. She received the Ph.D. degrees in computer science from the University of Science and Technology, Beijing, China. She held a post-doctoral fellowship with the Department of Computer Science, Tsinghua University, Beijing, China. She joined the School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, in July 2006, where she is currently a professor of computer science. She served as Chair and Co-Chair of IPC for many international and domestic academic conferences. She has been a Vice General Secretary of the Chinese Association for Artificial Intelligence. She was a visiting professor with the Department of Computer Science, Aarhus University, Aarhus, Denmark, from September 1996 to September 1997. Her current research interests include artificial intelligence, data mining, motion image processing, social network analysis and search, and computer application. (Email:junpingdu@126.com)
  • Received Date: 2018-11-12
  • Rev Recd Date: 2019-09-16
  • Publish Date: 2020-01-10
  • Discovering the hot topic among trends is an essential way to manage public opinion. But the dynamic property makes it a tough task as the existing methods of detection process are time-consuming. With the new Fast discovery of burst spatial-temporal topic (FDBST) method, the uptime of topic discovery is kept within one second and there is no sacrificing of topic quality, the noisy topics are all kept off, the time-varying problem and the sparsity problem are easily tackled by using spatial-temporal characteristics. How the FDBST works? It is triggered by a burst term based on social data trends, while the data is generated within a small time interval in a region while the irrelevant data is excluded during this process. By fusing the regional topics, the potential burst topic is obtained.The experiments show the preferable effects of the FDBST and it is an outperforms state-of-the-art approaches in terms of effectiveness.
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