FakeNewsTracker: a tool for fake news collection, detection, and visualization

Kai Shu, Deepak Mahudeswaran, Huan Liu

Research output: Contribution to journalArticlepeer-review

108 Scopus citations


Nowadays social media is widely used as the source of information because of its low cost, easy to access nature. However, consuming news from social media is a double-edged sword because of the wide propagation of fake news, i.e., news with intentionally false information. Fake news is a serious problem because it has negative impacts on individuals as well as society large. In the social media the information is spread fast and hence detection mechanism should be able to predict news fast enough to stop the dissemination of fake news. Therefore, detecting fake news on social media is an extremely important and also a technically challenging problem. In this paper, we present FakeNewsTracker, a system for fake news understanding and detection. As we will show, FakeNewsTracker can automatically collect data for news pieces and social context, which benefits further research of understanding and predicting fake news with effective visualization techniques.

Original languageEnglish (US)
Pages (from-to)60-71
Number of pages12
JournalComputational and Mathematical Organization Theory
Issue number1
StatePublished - Mar 1 2019


  • Fake news detection
  • Neural networks
  • Twitter visualization

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Computer Science(all)
  • Modeling and Simulation
  • Computational Mathematics
  • Applied Mathematics


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