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Author (up) Federico Angaramo; Claudio Rossi pdf 
  Title Online clustering and classification for real-time event detection in Twitter Type Conference Article
  Year 2018 Publication ISCRAM 2018 Conference Proceedings 15th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2018  
  Volume Issue Pages 1098-1107  
  Keywords Event detection; Social Media; Clustering; Machine Learning; Twitter  
  Abstract Event detection from social media is a challenging task due to the volume, the velocity and the variety of user-generated data requiring real-time processing. Despite recent works on this subject, a generalized and scalable approach that could be applied across languages and topics has not been consolidated, yet. In this paper, we propose a methodology for real-time event detection from Twitter data that allows users to select a topic of interest by defining a simple set of keywords and a matching rule. We implement the proposed methodology and evaluate it with real data to detect different types of events.  
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  Corporate Author Thesis  
  Publisher Rochester Institute of Technology Place of Publication Rochester, NY (USA) Editor Kees Boersma; Brian Tomaszewski  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN 978-0-692-12760-5 Medium  
  Track 1st International Workshop on Intelligent Crisis Management Technologies for Climate Events (ICMT) Expedition Conference ISCRAM 2018 Conference Proceedings 15th International Conference on Information Systems for Crisis Response and Management  
  Notes rossi@ismb.it Approved no  
  Call Number Serial 1630  
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