A preliminary approach to detect and track events in social media
Tang, Minyi
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https://hdl.handle.net/2142/90843
Description
Title
A preliminary approach to detect and track events in social media
Author(s)
Tang, Minyi
Issue Date
2016-04-28
Director of Research (if dissertation) or Advisor (if thesis)
Abdelzaher, Tarek F.
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(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.
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