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Author (up) Anjum, U.; Zadorozhny, V.; Krishnamurthy, P. pdf  doi
  Title Localization of Events Using Neural Networks in Twitter Data Type Conference Article
  Year 2023 Publication Proceedings of the 20th International ISCRAM Conference Abbreviated Journal Iscram 2023  
  Volume Issue Pages 909-919  
  Keywords Social Networking; Event Localization; Twitter; Neural Networks; GAN, BiLSTM  
  Abstract In this paper, we develop a model with neural networks to localize events using microblogging data. Localization is the task of finding the location of an event and can be done by discovering event signatures in microblogging data. We use the deep learning methodology of Bi-directional Long Short-Term Memory (Bi-LSTM) to learn event signatures. We propose a methodology for labeling the Twitter date for use in Bi-LSTM However, there might not be enough data available to train the Bi-LSTM and learn the event signatures. Hence, the data is augmented using generative adversarial networks (GAN). Finally, we combine event signatures at different temporal and spatial granularity to improve the accuracy of event localization. We use microblogging data collected from Twitter to evaluate our model and compare it with other baseline methods.  
  Address Tokyo Institute of Technology  
  Corporate Author Thesis  
  Publisher University of Nebraska at Omaha Place of Publication Omaha, USA Editor Jaziar Radianti; Ioannis Dokas; Nicolas Lalone; Deepak Khazanchi  
  Language English Summary Language Original Title  
  Series Editor Hosssein Baharmand Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition 1  
  ISSN ISBN Medium  
  Track AI for Crisis Management Expedition Conference  
  Notes Approved no  
  Call Number ISCRAM @ idladmin @ Serial 2575  
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