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Author Anna Kruspe; Jens Kersten; Friederike Klan pdf  isbn
  Title Detecting event-related tweets by example using few-shot models 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 media, Twitter, Relevance, Keywords, Hashtags, Few-shot models, One-class classification  
  Abstract Social media sources can be helpful in crisis situations, but discovering relevant messages is not trivial. Methods

have so far focused on universal detection models for all kinds of crises or for certain crisis types (e.g. floods).

Event-specific models could implement a more focused search area, but collecting data and training new models for

a crisis that is already in progress is costly and may take too much time for a prompt response. As a compromise,

manually collecting a small amount of example messages is feasible. Few-shot models can generalize to unseen

classes with such a small handful of examples, and do not need be trained anew for each event. We show how

these models can be used to detect crisis-relevant tweets during new events with just 10 to 100 examples and

counterexamples. We also propose a new type of few-shot model that does not require counterexamples.
  Address German Aerospace Center (DLR), Germany  
  Corporate Author Thesis  
  Publisher (up) 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 1911  
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