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Author (up) Jens Kersten; Anna Kruspe; Matti Wiegmann; Friederike Klan pdf 
  Title Robust filtering of crisis-related tweets 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 814-824  
  Keywords Filtering; Convolutional Neural Networks; Natural Disasters; Twitter; Model Transferability  
  Abstract Social media enables fast information exchange and status reporting during crises. Filtering is usually required to identify the small fraction of social media stream data related to events. Since deep learning has recently shown to be a reliable approach for filtering and analyzing Twitter messages, a Convolutional Neural Network is examined for filtering crisis-related tweets in this work. The goal is to understand how to obtain accurate and robust filtering models and how model accuracies tend to behave in case of new events. In contrast to other works, the application to real data streams is also investigated. Motivated by the observation that machine learning model accuracies highly depend on the used data, a new comprehensive and balanced compilation of existing data sets is proposed. Experimental results with this data set provide valuable insights. Preliminary results from filtering a data stream recorded during hurricane Florence in September 2018 confirm our results.  
  Address (1) German Aerospace Center (DLR), Germany; (2) Bauhaus-Universität Weimar  
  Corporate Author Thesis  
  Publisher Iscram Place of Publication Valencia, Spain Editor Franco, Z.; González, J.J.; Canós, J.H.  
  Language English Summary Language 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 jens.kersten@dlr.de; anna.kruspe@dlr.de; matti.wiegmann@uni-weimar.de; friederike.klan@dlr.de Approved no  
  Call Number Serial 1763  
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