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Author (up) Venkata Kishore Neppalli; Cornelia Caragea; Doina Caragea pdf  isbn
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  Title Deep Neural Networks versus Naive Bayes Classifiers for Identifying Informative Tweets during Disasters Type Conference Article
  Year 2018 Publication ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2018  
  Volume Issue Pages 677-686  
  Keywords deep neural networks, naive bayes classifiers, handcrafted features  
  Abstract In this paper, we focus on understanding the effectiveness of deep neural networks by comparison with the effectiveness of standard classifiers that use carefully engineered features. Specifically, we design various feature sets (based on tweet content, user details and polarity clues) and use these feature sets individually or in various combinations, with Naïve Bayes classifiers. Furthermore, we develop neural models based on Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) with handcrafted architectures. We compare the two types of approaches in the context of identifying informative tweets posted during disasters, and show that the deep neural networks, in particular the CNN networks, are more effective for the task considered.  
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  Corporate Author Thesis  
  Publisher Rochester Institute of Technology Place of Publication Rochester, NY (USA) Editor Kees Boersma; Brian Tomaszeski  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN 978-0-692-12760-5 Medium  
  Track Social Media Studies CO - Expedition Conference  
  Notes Approved no  
  Call Number Serial 2141  
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Author (up) Venkata Kishore Neppalli; Murilo Cerqueira Medeiros; Cornelia Caragea; Doina Caragea; Andrea Tapia; Shane Halse pdf  isbn
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  Title Retweetability Analysis and Prediction during Hurricane Sandy Type Conference Article
  Year 2016 Publication ISCRAM 2016 Conference Proceedings – 13th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2016  
  Volume Issue Pages  
  Keywords Twitter; Retweetability Analysis; Retweetability Prediction; Hurricane Sandy; Disaster Events  
  Abstract Twitter is a very important source for obtaining information, especially during events such as natural disasters. Users can spread information in Twitter either by crafting new posts, which are called “tweets,” or by using retweet mechanism to re-post the previously created tweets. During natural disasters, identifying how likely a tweet is to be highly retweeted is very important since it can help promote the spread of good information in a network such as Twitter, as well as it can help stop the spread of misinformation, when corroborated with approaches that identify trustworthy information or misinformation, respectively. In this paper, we present an analysis on retweeted tweets to determine several aspects affecting retweetability. We then extract features from tweets’ content and user account information and perform experiments to develop models that automatically predict the retweetability of a tweet in the context of the Hurricane Sandy.  
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  Corporate Author Thesis  
  Publisher Federal University of Rio de Janeiro Place of Publication Rio de Janeiro, Brasil Editor A. Tapia; P. Antunes; V.A. Bañuls; K. Moore; J. Porto  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3388 ISBN 978-84-608-7984-9 Medium  
  Track Social Media Studies Expedition Conference 13th International Conference on Information Systems for Crisis Response and Management  
  Notes Approved no  
  Call Number Serial 1389  
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