NDLI: A Framework for Online Hot Event Discovery on the Web

Content Provider	IEEE Xplore Digital Library
Author	Yang Liu Xiangfeng Luo
Copyright Year	2013
Description	Author affiliation: Sch. of Comput. Eng. & Sci., Shanghai Univ., Shanghai, China (Yang Liu; Xiangfeng Luo)
Abstract	With the coming era of Big Data, online hot event discovery has emerged to mine the social hot spots on the large-scale web resources. Hot events are naturally evolved over time, and in the meantime, their inherent semantic relations are likely to change. As a result, traditional event detection approaches do not perform well on the dynamic web resources. To overcome these bottlenecks, this paper presents a novel hot event discovery framework to detect hot events online, containing three stages: 1) document preprocessing which selects significant features to represent document content, 2) threshold-resilient document classification, which classifies the incoming documents into topically related events considering event evolution, 3) adaptive splitting document clustering, which is used to timely cluster newly happened hot events. Using online data set from Baidu website, the experiments demonstrate the hot events discovery ability with respect to high accuracy, good scalability and short runtime.
Starting Page	989
Ending Page	996
File Size	1863156
Page Count	8
File Format	PDF
ISBN	9780769550961
DOI	10.1109/CSE.2013.145
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-03
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Event detection threshold-resilient classification Clustering methods online event detection Communities Semantics Clustering algorithms adaptive splitting clustering Timing event discovery framework
Content Type	Text
Resource Type	Article

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