Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
TwitterMonitor: trend detection over the twitter stream
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Emerging topic detection on Twitter based on temporal and social terms evaluation
Proceedings of the Tenth International Workshop on Multimedia Data Mining
A Novel Approach for Event Detection by Mining Spatio-temporal Information on Microblogs
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
Cost-effective node monitoring for online hot eventdetection in sina weibo microblogging
Proceedings of the 22nd international conference on World Wide Web companion
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Microblogging has become a popular means of communication and information diffusion. Due to the huge amount of microblogs generated daily, the communication and computing costs required for real hot event detection is a big challenge. Choosing a small subnet of nodes to detect events has received increasing research interests in recent years. But the previous methods manage to select nodes to cover all the events including less popular events in sample datasets under the limited subnet size, which cause a big difference of event detection ratio between sample events and online real events in microblogs. In this paper we propose a new subnet nodes selection scheme based on the event detection ratio and nodes' events participation probabilities. Under the requirement of average event detection ratio, we prefer to choose the nodes who are active in propagating hot events than the nodes who participate in the less popular events. And we take dynamic programming to accelerate the computing. The experimental results show that our proposed method has a better performance.