Learning to explore spatio-temporal impacts for event evaluation on social media
Pages 316 - 325
Abstract
Due to the explosive growth of social-media applications, enabling event-awareness by social mining has become extremely important. The contents of microblogs preserve valuable information associated with past disastrous events and stories. To learn the experiences from past microblogs for tackling emerging real-world events, in this work we utilize the social-media messages to characterize events through their contents and spatio-temporal features for relatedness analysis. Several essential features of each detected event dataset have been extracted for event formulation by performing content analysis, spatial analysis, and temporal analysis. This allows our approach compare the new event vector with existing event vectors stored in the event-data repository for evaluation of event relatednesss, by means of validating spatio-temporal feature factors involved in the event evolution. Through the developed algorithms for computing event relatedness, in our system the ranking of related events can be computed, allowing for predicting possible evolution and impacts of the event. The developed system platform is able to immediately evaluate the significantly emergent events, in order to achieve real-time knowledge discovery of disastrous events.
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Index Terms
- Learning to explore spatio-temporal impacts for event evaluation on social media
Index terms have been assigned to the content through auto-classification.
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Sponsors
- NSF of China: National Natural Science Foundation of China
- APNNA: Asia Pacific Neural Network Assembly
- IEEE Computational Intelligence Society: IEEE Computational Intelligence Society
- IEEE Harbin Section: IEEE Harbin Section
- The European Neural Network Society
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 11 July 2012
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