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Author (down) Asmelash Teka Hadgu; Sallam Abualhaija; Claudia Niederée pdf  isbn
openurl 
  Title Real-time Adaptive Crawler for Tracking Unfolding Events on Twitter Type Conference Article
  Year 2019 Publication Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management Abbreviated Journal Iscram 2019  
  Volume Issue Pages  
  Keywords social search, real-time adaptive search, event tracking, crsis communication  
  Abstract When a major event such as a crisis situation occurs, people post messages on social media sites such as Twitter, in

order to exchange information or to share emotions. These posts can provide useful information to raise situation

awareness and support decision making, e.g., by aid organizations. In this paper, we propose a novel method for

social media crawling, which exploits a Bayesian inference framework to keep track of keyword changes over time

and uses a counter-stream to gauge the inclusion of noise and irrelevant information. In addition, we present a

framework to evaluate real-time adaptive social search algorithms in a reproducible manner, which relies on a

semi-automated approach for ground-truth construction. We show that our method outperforms previous methods

for very large scale events.
 
  Address L3S Research Center, Leibniz universität Hannover, Germany;Interdisciplinary Centre for Security, Reliability and Trust  
  Corporate Author Thesis  
  Publisher Iscram Place of Publication Valencia, Spain Editor Franco, Z.; González, J.J.; Canós, J.H.  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN 978-84-09-10498-7 Medium  
  Track T8- Social Media in Crises and Conflicts Expedition Conference 16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019)  
  Notes Approved no  
  Call Number Serial 1985  
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