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Author (up) Ahmed Abouzeid(1, 2); Ole-Christoffer Granmo(1, 2); Christian Webersik(2) Morten Goodwin(1, 2) pdf 
  Title Distributed Learning Automata-based Intelligence for Propagation-based Fake News Detection on Social Media During Crisis 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 1403-1404  
  Keywords  
  Abstract One of the recently revealed approaches to social media fake news detection is the propagation-based approach. The assumption is that information trustworthiness evidence could be unveiled by investigating different opinions and contradictions. In propagation-based methods, credibility networks are built to employ optimization techniques over different pieces of news giving the underlying point of view. Mining different viewpoints and reactions to news are referred to as stance detection tasks. However, stance detection models are not either robust enough nor generalized on different datasets, since they are highly dependent on the specific training data feature space. That introduces the first challenge to propagation-based fake news detection. Another challenging and maybe avoided issue is the problem of biased opinions or accounts.  
  Address (1) CAIR – University of Agder; (2) CIEM – University of Agder  
  Corporate Author Thesis  
  Publisher Iscram Place of Publication Valencia, Spain Editor Franco, Z.; González, J.J.; Canós, J.H.  
  Language English Summary Language 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 Poster Session Expedition Conference 16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019)  
  Notes ahmed.abouzeid@uia.no; ole.granmo@uia.no; christian.webersik@uia.no; morten.goodwin@uia.no Approved no  
  Call Number Serial 1831  
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