Skip to main content

Using Machine Learning for News Verification

  • Conference paper
  • First Online:
Future Data and Security Engineering (FDSE 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11251))

Included in the following conference series:

  • 1024 Accesses

Abstract

The news fakes are issued with the intention of misleading, manipulating personal decisions, discredit or exalt an institution, entity or person or obtain economic gains or political revenue. They are related to propaganda and post-truth. Fake news, by presenting falsehoods as if they were real, are considered a threat to the credibility of serious media and professional journalists. The dissemination of false news in order to influence the behavior of a community has antecedents since antiquity, but given that its scope is directly related to the means of reproduction of information specific to each historical stage, its area and speed of propagation was scarce in the historical stages prior to the appearance of the mass media.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Mauri, M., Jonathan, G., Tommaso, V., Michele, M.: A field guide to fake news (2017)

    Google Scholar 

  2. Mele, N., Lazer, D., Baum, M., Grinberg, N., Friedland, L., Joseph, K., Hobbs, W., Mattsson, C.: Combating fake news: an agenda for research and action, May 2017

    Google Scholar 

  3. Gerazov, B., Conceicao, R.C.: Deep learning for tumour classification in homogeneous breast tissue in medical microwave imaging. In: IEEE EUROCON (2017)

    Google Scholar 

  4. Affonso, C., Rossi, A.L.D., Vieira, F.H.A., de Carvalho, A.C.P.D.L.F.: Deep learning for biological image classification. Expert Syst. Appl. 85, 114122 (2017). https://doi.org/10.1016/j.eswa.2017.05.039

    Article  Google Scholar 

  5. Yudin, D., Zeno, B.: Event recognition on images by fine-tuning of deep neural networks (2018). https://doi.org/10.1007/978-3319-68321-8_49

  6. Shu, K., Wang, S., Sliva, A., Tang, J., Liu, H.: Fake news detection on social media: a data mining perspective. ACM SIGKDD Explor. Newslett. 19 (2017)

    Article  Google Scholar 

  7. Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots (2017). arXiv:1707.07592

  8. Bajaj, S.: The Pope Has a New Baby! Fake News Detection Using Deep Learning (n.d.). https://web.stanford.edu/class/cs224n/reports/2710385.pdf

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Octavio José Salcedo Parra .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Agudelo, G.E.R., Parra, O.J.S., Medina, J. (2018). Using Machine Learning for News Verification. In: Dang, T., Küng, J., Wagner, R., Thoai, N., Takizawa, M. (eds) Future Data and Security Engineering. FDSE 2018. Lecture Notes in Computer Science(), vol 11251. Springer, Cham. https://doi.org/10.1007/978-3-030-03192-3_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-03192-3_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03191-6

  • Online ISBN: 978-3-030-03192-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics