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2016 49th Hawaii International Conference on System Sciences (HICSS) (2016)
Koloa, HI, USA
Jan. 5, 2016 to Jan. 8, 2016
ISSN: 1530-1605
ISBN: 978-0-7695-5670-3
pp: 3739-3748
ABSTRACT
Online News has become one of the most popular channels for consuming and understanding the real-world events. However, it is increasingly difficult for users to hold the full picture of massive events in a comprehensive perspective. In addition, how to motivate human to perceive the important rare events, which may trigger the subsequent emergency events, remains to be a challenge. To address these issues, we present EventPanorama, a framework to detect and visualize events. Firstly, we introduce a hybrid event detection method which combines topic modeling and Chance Discovery, and detects events more effectively by coupling multiple term-relations. Secondly, we propose a heterogeneous event-graph layout algorithm which takes the significance of events into consideration by leveraging latent co-occurrence relations to represent important rare events and thus enhance human cognition. An experiment demonstrates the superiority of EventPanorama by comparing with several benchmarks.
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CITATION

C. Zhang et al., "EventPanorama: A Framework for Event Detection and Visualization from Online News," 2016 49th Hawaii International Conference on System Sciences (HICSS), Koloa, HI, USA, 2016, pp. 3739-3748.
doi:10.1109/HICSS.2016.466
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