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

  • Kai Shu
  • Deepak Mahudeswaran
  • Huan Liu
S.I. : SBP-BRiMS 2018
  • 215 Downloads

Abstract

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.

Keywords

Fake news detection Neural networks Twitter visualization 

Notes

Acknowledgements

This material is based upon work supported by, or in part by, the ONR Grant N00014-16-1-2257, ARO (W911NF-15-1-0328) and ONR N000141310835.

References

  1. Bowman SR, et al (2015) Generating sentences from a continuous space. arXiv:1511.06349
  2. Castillo C, Mendoza M, Poblete B (2011) Information credibility on twitter. In: Proceedings of the 20th international conference on World wide web. ACMGoogle Scholar
  3. Le Q, Mikolov T (2014) Distributed representations of sentences and documents. International conference on machine learning, pp 1188–1196Google Scholar
  4. Luke S, Morgan J (2015) Who tweets with their location? Understanding the relationship between demographic characteristics and the use of geoservices and geotagging on Twitter. PLoS ONE 10(11):e0142209CrossRefGoogle Scholar
  5. Morstatter F, et al (2013) Understanding twitter data with tweetxplorer. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining. ACMGoogle Scholar
  6. Ruchansky, N, Seo S, Liu Y (2017) CSI: a hybrid deep model for fake news detection. In: Proceedings of the 2017 ACM on conference on information and knowledge management. ACMGoogle Scholar
  7. Shu, K, Wang S, Liu H (2017a) Exploiting tri-relationship for fake news detection. arXiv:1712.07709Google Scholar
  8. Shu K et al (2017b) Fake news detection on social media: a data mining perspective. ACM SIGKDD Explor Newslett 19(1):22–36CrossRefGoogle Scholar
  9. Shu, K, Bernard HR, Liu H (2018a) Studying fake news via network analysis: detection and mitigation. arXiv:1804.10233
  10. Shu, K, Wang S, Liu H (2018b) Understanding user profiles on social media for fake news detection. In: IEEE conference on multimedia information processing and retrieval (MIPR), pp 430–435Google Scholar
  11. Shu K, Mahudeswaran D, Wang S, Lee D, Liu H (2018c) FakeNewsNet: A Data Repository with News Content, Social Context and Dynamic Information for Studying Fake News on Social Media. arXiv:1809.01286
  12. Wang WY (2017) “liar, liar pants on fire”: a new benchmark dataset for fake news detection. arXiv:1705.00648

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Arizona State UniversityTempeUSA

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