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News labeling as early as possible: Real or fake?

Ramezani, M ; Sharif University of Technology | 2019

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  1. Type of Document: Article
  2. DOI: 10.1145/3341161.3342957
  3. Publisher: Association for Computing Machinery, Inc , 2019
  4. Abstract:
  5. Differentiating between real and fake news propagation through online social networks is an important issue in many applications. The time gap between the news release time and detection of its label is a significant step towards broadcasting the real information and avoiding the fake. Therefore, one of the challenging tasks in this area is to identify fake and real news in early stages of propagation. However, there is a tradeoff between minimizing the time gap and maximizing accuracy. Despite recent efforts in detection of fake news, there has been no significant work that explicitly incorporates early detection in its model. The proposed method utilizes recurrent neural networks with a novel loss function, and a new stopping rule. Experiments on real datasets demonstrate the effectiveness of our model both in terms of early labelling and accuracy, compared to the state of the art baseline and models. © 2019 Association for Computing Machinery
  6. Keywords:
  7. Early news labeling ; Fake news ; Online social networks ; Recurrent neural networks ; Backpropagation ; Social networking (online) ; Loss functions ; On-line social networks ; Real data sets ; Release time ; State of the art ; Stopping rule ; Time gap
  8. Source: 11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019, 27 August 2019 through 30 August 2019 ; 2019 , Pages 536-537 ; 9781450368681 (ISBN)
  9. URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078834668&doi=10.1145%2f3341161.3342957&partnerID=40&md5=5d52c5fde247fa05e073032e228f95b3