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Title:A preliminary approach to detect and track events in social media
Author(s):Tang, Minyi
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Social Event Tracking
Data Mining
Abstract:Many algorithms have been proposed to model spatiotemporal events in both sensor network and social networks. However, most of them can not fullfil the task in a social network data streaming context. We proposed an evolving Mean Shift clustering based algorithm to formulate a robust system to automatically detect and track events in social network media. We also demonstrate its performance in empirical experiments. Our online system can be udapted and maintained without comsuming too much system resources which may formulate a good basis for event detection and tracking in the domain of real-time social network media.
Issue Date:2016-04-28
Rights Information:Copyright 2016 Minyi Tang
Date Available in IDEALS:2016-07-07
Date Deposited:2016-05

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