Cited By
View all- Huang WWang YHuang JCheng X(2023)Visualization Analysis of Hot Event Propagation Topic MapHuman Centered Computing10.1007/978-3-031-23741-6_15(161-167)Online publication date: 1-Jan-2023
Learning Topic Map from Large Scale Social Media Data
Pages 279 - 283
Abstract
Geo-tagged social media data are hugely generated every day. Those content provide rich sources to explore keywords and topics in any region of the world thanks to the widely popularized mobile internet services. The association between geographic regions and their keywords/topics in social media has raised a lot of research attentions. Such association provides important information to event prediction, source detection, and news propagation in various applications. For instance, the disaster control and management, and evaluation of sales marketing campaign are just two of the many examples. The association between regions and keywords/topics are analogous to that between geographic coordinates and spatial features, such as streets intersections, building compounds and so on in a conventional street map, and we propose a new model, called “topic map” to mimic such an analogy and to encode and represent text features with geographic regions that reside in Geo-tagged social media data. Applications based on topic map will be explored during my research, and extensions to temporal data will be investigated further.
References
[1]
Christopher M Bishop. 2006. Pattern recognition and machine learning. Springer-Verlag New York.
[2]
David M Blei, Alp Kucukelbir, and Jon D McAuliffe. 2017. Variational inference: A review for statisticians. J. Amer. Statist. Assoc. 112, 518 (2017), 859–877.
[3]
David M Blei and John D Lafferty. 2006. Dynamic topic models. In Proceedings of the 23rd international conference on Machine learning. ACM, 113–120.
[4]
David M Blei and John D Lafferty. 2007. A correlated topic model of science. The Annals of Applied Statistics(2007), 17–35.
[5]
David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. Latent dirichlet allocation. Journal of machine Learning research 3, Jan (2003), 993–1022.
[6]
Jacob Eisenstein, Brendan O’Connor, Noah A Smith, and Eric P Xing. 2010. A latent variable model for geographic lexical variation. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 1277–1287.
[7]
Matthew Hoffman, Francis R Bach, and David M Blei. 2010. Online learning for latent dirichlet allocation. In advances in neural information processing systems. 856–864.
[8]
Liangjie Hong, Amr Ahmed, Siva Gurumurthy, Alexander J Smola, and Kostas Tsioutsiouliklis. 2012. Discovering geographical topics in the twitter stream. In Proceedings of the 21st international conference on World Wide Web. ACM, 769–778.
[9]
Barbara Johnstone. 2010. Language and place. In R. Mesthrie and W. Wolfram, editors, Cambridge Handbook of Sociolinguistics. Cambridge University Press.
[10]
Christoph Carl Kling, Jérôme Kunegis, Sergej Sizov, and Steffen Staab. 2014. Detecting non-gaussian geographical topics in tagged photo collections. In Proceedings of the 7th ACM international conference on Web search and data mining. ACM, 603–612.
[11]
Qiaozhu Mei, Chao Liu, Hang Su, and ChengXiang Zhai. 2006. A probabilistic approach to spatiotemporal theme pattern mining on weblogs. In Proceedings of the 15th international conference on World Wide Web. ACM, 533–542.
[12]
Takeshi Sakaki, Makoto Okazaki, and Yutaka Matsuo. 2010. Earthquake shakes Twitter users: real-time event detection by social sensors. In Proceedings of the 19th international conference on World wide web. ACM, 851–860.
[13]
Zhijun Yin, Liangliang Cao, Jiawei Han, Chengxiang Zhai, and Thomas Huang. 2011. Geographical topic discovery and comparison. In Proceedings of the 20th international conference on World wide web. ACM, 247–256.
[14]
Quan Yuan, Gao Cong, Zongyang Ma, Aixin Sun, and Nadia Magnenat Thalmann. 2013. Who, where, when and what: discover spatio-temporal topics for twitter users. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 605–613.
[15]
Chao Zhang, Keyang Zhang, Quan Yuan, Haoruo Peng, Yu Zheng, Tim Hanratty, Shaowen Wang, and Jiawei Han. 2017. Regions, periods, activities: Uncovering urban dynamics via cross-modal representation learning. In Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 361–370.
[16]
Chao Zhang, Guangyu Zhou, Quan Yuan, Honglei Zhuang, Yu Zheng, Lance Kaplan, Shaowen Wang, and Jiawei Han. 2016. Geoburst: Real-time local event detection in geo-tagged tweet streams. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 513–522.
Index Terms
- Learning Topic Map from Large Scale Social Media Data
Index terms have been assigned to the content through auto-classification.
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April 2020
854 pages
ISBN:9781450370240
DOI:10.1145/3366424
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Published: 20 April 2020
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View all- Huang WWang YHuang JCheng X(2023)Visualization Analysis of Hot Event Propagation Topic MapHuman Centered Computing10.1007/978-3-031-23741-6_15(161-167)Online publication date: 1-Jan-2023
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