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2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2018)
Barcelona, Spain
Aug. 28, 2018 to Aug. 31, 2018
ISSN: 2473-9928
ISBN: 978-1-5386-6052-2
pp: 429-436
Hengtong Zhang , SUNY Buffalo, Department of Computer Science and Engineering, Buffalo, NY, USA
Fenglong Ma , SUNY Buffalo, Department of Computer Science and Engineering, Buffalo, NY, USA
Yaliang Li , SUNY Buffalo, Department of Computer Science and Engineering, Buffalo, NY, USA
Chao Zhang , SUNY Buffalo, Department of Computer Science and Engineering, Buffalo, NY, USA
Tianqi Wang , SUNY Buffalo, Department of Computer Science and Engineering, Buffalo, NY, USA
Yaqing Wang , SUNY Buffalo, Department of Computer Science and Engineering, Buffalo, NY, USA
Jing Gao , SUNY Buffalo, Department of Computer Science and Engineering, Buffalo, NY, USA
Lu Su , SUNY Buffalo, Department of Computer Science and Engineering, Buffalo, NY, USA
ABSTRACT
Detecting local events (e.g., protests, accidents) in real-time is an important task needed by a wide spectrum of real-world applications. In recent years, with the proliferation of social media platforms, we can access massive geo- tagged social messages, which can serve as a precious resource for timely local event detection. However, existing local event detection methods either suffer from unsatisfactory performances or need intensive annotations. These limitations make existing methods impractical for large-scale applications. Through the analysis of real-world datasets, we found that the informativeness level of social media users, which is neglected by existing work, plays a highly critical role in distilling event-related information from noisy social media contexts. Motivated by this finding, we propose an unsupervised framework, named LEDetect, to estimate the informativeness level of social media users and leverage the power of highly informative users for local event detection. Experiments on a large-scale real-world dataset show that the proposed LEDetect model can improve the performance of event detection compared with the state-of-the-art unsupervised approach. Also, we use case studies to show that the events discovered by the proposed model are of high quality and the extracted highly informative users are reasonable.
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CITATION

H. Zhang et al., "Leveraging the Power of Informative Users for Local Event Detection," 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Barcelona, Spain, 2018, pp. 429-436.
doi:10.1109/ASONAM.2018.8508618
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