FDBST: Fast Discovery of Bursty Spatial-Temporal Topic

ZHU Chuangying; DU Junping; ZHANG Qiang; SHI Lei; Lee JangMyung

doi:10.1049/cje.2019.12.002

Volume 29 Issue 1

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Electronics > 2020 > 29(1): 168-176

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

Citation:

PDF( 2910 KB)

FDBST: Fast Discovery of Bursty Spatial-Temporal Topic

doi: 10.1049/cje.2019.12.002

1.
Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China;
2.
Research Institute of Computer, Information and Communication, Pusan National University, Pusan 999007, the Republic of Korea

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).

More Information

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

Abstract

Abstract

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.
- Social network,
- Real-time,
- Spatialtemporal,
- Burst topic,
- Data stream

FullText(HTML)

References(28)

References

F. Kou, J. Du, Z. Lin, et al., "A semantic modeling method for social network short text based on spatial and temporal characteristics", Journal of Computational Science, Vol.28, pp.281-293, 2018.

X. Cheng, X. Yan, Y. Lan, et al., "BTM:Topic modeling over short texts".IEEE Transactions on Knowledge and Data Engineering, Vol.26, No.12, pp.2928-2941, 2014.

L. Shi, J. Du, M. Liang, "Social network bursty topic discovery based on RNN and topic model", Journal of Communications, Vol.39, No.04, pp.189-198, 2018. (in Chinese)

G. Stilo and P. Velardi, "Time makes sense:Event discovery in twitter using temporal similarity", Proceedings of the 2014 Ieee/wic/acm International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), Warsaw, Poland, pp.186-193, 2014.

F. Kou, J. Du, Y. He, et al., "Social network search based on semantic analysis and learning", Caai Transactions on Intelligence Technology, Vol.1, No.4, pp.293-302, 2016.

C. Zhu and J. Du, "Background feature clustering and its application to social text", Information Processing Letters, Vol.136, pp.44-48, 2018.

W. Xie, F. Zhu, J. Jiang, et al., "Topicsketch:Realtime bursty topic detection from twitter", IEEE Transactions on Knowledge and Data Engineering, Vol.28, No.8, pp.2216-2229, 2016.

D. He and D.S. Parker, "Topic dynamics:an alternative model of bursts in streams of topics", Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, Washington, DC, USA, pp.443-452, 2010.

P. Lv, C. Ji, X. Wang, et al., "Mass of short texts topical Hierarchy mining integrated anchor extraction", Chinese Journal of Electronics, Vol.46, No.5, pp.1084-1088, 2018. (in Chinese)

W. Cui, J. Du, D. Wang, et al., "Extended search method based on a semantic hashtag graph combining social and conceptual information", World Wide Web, Vol.22, No.6, pp.2589-2610, 2019.

X. Li, C. Li, J. Chi, et al., "Short text topic modeling by exploring original documents", Knowledge and Information Systems, Vol.56, No.2, pp.443-462, 2018.

W. Liang, R. Feng, X. Liu, et al., "GLTM:A global and local word embedding-based topic model for short texts", IEEE Access, Vol.6, pp.43612-43621, 2018.

J. Chen, X. Liu, B. Li, et al., "Personalized microblogging recommendation based on dynamic interests and social networking of users", Chinese Journal of Electronics, Vol.45, No.4, pp.898-905, 2017. (in Chinese)

P. Lv, X. Meng, Y. Zhang, "FeRe:Exploiting influence of multi-dimensional features resided in news domain for recommendation", Information Processing and Management, Vol.53, No.5, pp.1215-1241, 2017.

F. Kou, J. Du, C. Yang, et al., "Hashtag recommendation based on multi-features of microblogs". Journal of Computer Science and Technology, Vol.33, No.4, pp.711-726, 2018.

W. Wang, X. Zhang, G. Ren, et al., "Predicting microblog user retweet behaviors based on energy optimization", Chinese Journal of Electronics, Vol.45, No.12, pp.2987-2996, 2017. (in Chinese)

Q. Li, L. Liu, M. Xu, et al., "GDTM:A Gaussian dynamic topic model for forwarding prediction under complex mechanisms". IEEE Transactions on Computational Social Systems, Vol.6, No.2, pp.338-349, 2019.

D.M. Blei and J.D. Lafferty, "Dynamic topic models", Proc of the 23rd International Conference on Machine Learning, Pittsburgh, Pennsylvania, USA, 2006.

D.M. Blei, "Probabilistic topic models", Communications of the ACM, Vol.55, No.4, pp.77-84, 2012.

P. Jähnichen, F. Wenzel, M. Kloft, et al., "Scalable generalized dynamic topic models", Proceedings of the 21st International Conference on Artificial Intelligence and Statistics, Lanzarote, Spain, pp.1427-1435, 2018.

Z. Gou, L. Han, L. Sun, et al., "Constructing dynamic topic models based on variational antoencoder and factor graph", IEEE Access, Vol.6, pp.53012-53111, 2018.

X. Yan, J. Guo, Y. Lan, et al., "A probabilistic model for bursty topic discovery in microblogs", Proc of the 29th AAAI Conference on Artificial Intelligence, Austin, Texas, USA, pp.353-359, 2015.

M. Cataldi, L.D. Caro, C. Schifanella, "Twitter as a personalizable information service", Multimedia Data Mining and Analytics, Springer, Cham, pp.61-91, 2015.

J. Huang, M. Peng, H. Wang, et al., "A probabilistic method for emerging topic tracking in Microblog stream", World Wide Web-internet and Web Information Systems, Vol.20, No.2, pp.325-350, 2017.

Y. Qin, D. Wurzer and C. Tang. "Predicting future rumours", Chinese Journal of Electronics, Vol.27, No.3, pp.514-520, 2018.

T. Zhang, B. Zhou, J. Huang, et al., "A refined method for detecting interpretable and real-time bursty topic in microblog stream", International Conference on Web Information Systems Engineering, Puschino, Russia, pp.3-17, 2017.

Y. Wan and J. Sui. "Bursty topic detection method for microblog based on influence from user behaviors", Journal of university of science and technology of China, Vol.47, No.4, pp.328-335, 2017. (in Chinese)

K.C. Lee, C.H. Hsieh, L.J. Wei, et al., "Sec-Buzzer:Cyber security emerging topic mining with open threat intelligence retrieval and timeline event annotation", Soft Computing, Vol.21, No.11, pp.2883-2896, 2017.

Relative Articles

Supplements(0)

Cited By

Proportional views