Information Abstraction from Crises Related Tweets Using Recurrent Neural Network

  • Mehdi Ben Lazreg
  • Morten Goodwin
  • Ole-Christoffer Granmo
Conference paper

DOI: 10.1007/978-3-319-44944-9_38

Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 475)
Cite this paper as:
Ben Lazreg M., Goodwin M., Granmo OC. (2016) Information Abstraction from Crises Related Tweets Using Recurrent Neural Network. In: Iliadis L., Maglogiannis I. (eds) Artificial Intelligence Applications and Innovations. AIAI 2016. IFIP Advances in Information and Communication Technology, vol 475. Springer, Cham

Abstract

Social media has become an important open communication medium during crises. The information shared about a crisis in social media is massive, complex, informal and heterogeneous, which makes extracting useful information a difficult task. This paper presents a first step towards an approach for information extraction from large Twitter data. In brief, we propose a Recurrent Neural Network based model for text generation able to produce a unique text capturing the general consensus of a large collection of twitter messages. The generated text is able to capture information about different crises from tens of thousand of tweets summarized only in a 2000 characters text.

Keywords

Information abstraction Recurrent neural network Twitter data Crisis management 

Copyright information

© IFIP International Federation for Information Processing 2016

Authors and Affiliations

  • Mehdi Ben Lazreg
    • 1
  • Morten Goodwin
    • 1
  • Ole-Christoffer Granmo
    • 1
  1. 1.University of AgderGrimstadNorway

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