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Author Anna Kruspe pdf  isbn
openurl 
  Title Detecting Novelty in Social Media Messages During Emerging Crisis Events Type Conference Article
  Year 2020 Publication ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages (down) 860-871  
  Keywords Social media; Clustering; Novelty; Embeddings  
  Abstract Social media can be a highly valuable source of information during disasters. A crisis' development over time is of particular interest here, as social media messages can convey unfolding events in near-real time. Previous approaches for the automatic detection of information in such messages have focused on a static analysis, not taking temporal changes and already-known information into account. In this paper, we present a novel method for detecting new topics in incoming Twitter messages (tweets) conditional upon previously found related tweets. We do this by first extracting latent representations of each tweet using pre-trained sentence embedding models. Then, Infinite Mixture modeling is used to dynamically cluster these embeddings anew with each incoming tweet. Once a cluster reaches a minimum number of members, it is considered to be a new topic. We validate our approach on the TREC Incident Streams 2019A data set.  
  Address German Aerospace Center (DLR), Jena, Germany  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-76 ISBN 2411-3462 Medium  
  Track Social Media for Disaster Response and Resilie Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes anna.kruspe@dlr.de Approved no  
  Call Number Serial 2277  
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Author Jens Kersten; Anna Kruspe; Matti Wiegmann; Friederike Klan pdf  isbn
openurl 
  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 (down)  
  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 German Aerospace Center (DLR), Germany;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 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 1909  
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Author Anna Kruspe; Jens Kersten; Friederike Klan pdf  isbn
openurl 
  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 (down)  
  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 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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