Skip to main content
SpringerLink
Log in

A Machine Learning Approach to Fake News Detection Using Knowledge Verification and Natural Language Processing

  • Conference paper
  • First Online:
Advances in Intelligent Networking and Collaborative Systems (INCoS 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1035))

Abstract

The term “fake news” gained international popularity as a result of the 2016 US presidential election campaign. It is related to the practice of spreading false and/or misleading information in order to influence popular opinion. This practice is known as disinformation. It is one of the main weapons used in information warfare, which is listed as an emerging cybersecurity threat. In this paper, we explore “fake news” as a disinformation tool. We survey previous efforts in defining and automating the detection process of “fake news”. We establish a new fluid definition of “fake news” in terms of relative bias and factual accuracy. We devise a novel framework for fake news detection, based on our proposed definition and using a machine learning model.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
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. Symantec Internet Security Threat Report 2019. https://www.symantec.com/en/sg/security-center/threat-report. Accessed 07 Mar 2019

  2. Tzu, S.: The Art of War. China. 5th Century BC

    Google Scholar 

  3. Kaiser, D.: Politics and War: European Conflict from Philip I1 to Hitler, pp. 149 and 172. Harvard University Press, Cambridge (1990)

    Google Scholar 

  4. Coleman, S.: The elusiveness of political truth: from the conceit of objectivity to intersubjective judgement. Eur. J. Commun. 33(2), 157–171 (2018). https://doi.org/10.1177/0267323118760319

    Article  Google Scholar 

  5. Abrams, S.: Beyond propaganda: soviet active measures in Putin’s Russia. Connections 15(1), 5–31 (2016). http://www.jstor.org/stable/26326426

    Google Scholar 

  6. Wall, T.: U.S. Psychological Warfare and Civilian Targeting. Peace Rev. 22(3), 288–294 (2010). SocINDEX with Full Text. Web. 20 Feb. 2015

    Article  Google Scholar 

  7. Fried, D., Polyakova, A.: Democratic defence against disinformation. https://www.atlanticcouncil.org/images/publications/Democratic_Defense_Against_Disinformation_FINAL.pdf. Accessed 04 Apr 2019

  8. Correra, G.: Bulgaria warns of Russian attempts to divide Europe. BBC (2016). https://www.bbc.com/news/world-europe-37867591. Accessed 19 Feb 2019

  9. Parkinson, J., Kantchev, G.: Wall Street J. (2017). https://www.wsj.com/articles/how-does-russia-meddle-in-elections-look-at-bulgaria-1490282352. Accessed 19 Feb 2019

  10. Vassilev, I.: Russia’s elections manual for BSP and the role played by Bulgarian sociological agencies. Bulgaria Analytica (2017). http://bulgariaanalytica.org/en/2017/03/31/

  11. Sputnik News: While West Claims Moscow is Meddling in Bulgarian Elections, Sofia Blames Turkey (2017). https://sputniknews.com/europe/201703251051961027-bulgarian-election-meddling-accusations/, Retrieved 04.04.2019

  12. Blyskov, P.: Dmitry Medvedev’s interview with Bulgarian newspaper Trud. http://government.ru/en/news/35903/. Accessed 10 Apr 2019

  13. Edson, C., Tandoc, Jr., Lim, Z.W., Ling, R.: Defining fake news. Digit. J. 6(2), 137–153 (2018). https://doi.org/10.1080/21670811.2017.1360143

    Article  Google Scholar 

  14. Lazer, D., et al.: The science of fake news. Science 359(6380), 1094–1096 (2018)

    Article  Google Scholar 

  15. Horne, B.D., Adali, S.: This just in: fake news packs a lot in title, uses simpler, repetitive content in text body, more similar to satire than real news. arXiv preprint arXiv:1703.09398 (2017)

  16. Wang, P., Angarita, R., Renna, I.: Is this the era of misinformation yet: combining social bots and fake news to deceive the masses. In: Companion Proceedings of the Web Conference 2018 (WWW 2018). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, pp. 1557–1561 (2018)

    Google Scholar 

  17. https://www.nytimes.com/2002/10/22/us/ap-says-it-couldn-t-find-45-of-fired-writer-s-sources.html. Accessed 10 Apr 2019

  18. Factcheck. https://www.factcheck.org/about/our-mission/. Accessed 02 Apr 2019

  19. Snopes. https://www.snopes.com/about-snopes/. Accessed 02 Apr 2019

  20. PolitiFact. https://www.politifact.com/truth-o-meter/article/2018/feb/12/principles-truth-o-meter-politifacts-methodology-i/. Accessed 02 Apr 2019

  21. Thorne, J., Vlachos, A.: Automated fact checking: task formulations, methods and future directions. In: Proceedings of the 27th International Conference on Computational Linguistics (COLING 2018) (2018)

    Google Scholar 

  22. https://www.poynter.org/fact-checking/2018/google-suspends-fact-checking-feature-over-quality-concerns/. Accessed 10 Apr 2019

  23. O’Brien, N., Latessa, S., Evangelopoulos, G., Boix, X.: The language of fake news: opening the black-box of deep learning based detectors. In: Workshop on AI for Social Good, NIPS 2018 (2018). http://hdl.handle.net/1721.1/120056

  24. Rashkin, H., Choi, E., Jang, J.Y., Volkova, S., Choi, Y.: Truth of varying shades: analyzing language in fake news and political fact-checking. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 2931–2937 (2017)

    Google Scholar 

  25. Long, Y., Lu, Q., Xiang, R., Li, M., Huang, C.R.: Fake news detection through multi-perspective speaker profiles. In: Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (2017)

    Google Scholar 

  26. Oshikawa, R., Qian, J., Wang, W.Y.: A Survey on Natural Language Processing for Fake News Detection. Computation and Language (2018). arXiv:1811.00770v1 [cs.CL]

  27. Gröndahl, T., Asokan, N.: Text Analysis in Adversarial Settings: Does Deception Leave a Stylistic Trace? Computation and Language (2019). arXiv:1902.08939v2 [cs.CL]

    Article  Google Scholar 

  28. Conroy, N.J., Chen, Y., Rubin, V.L.: Automatic deception detection: methods for finding fake news. In: The Proceedings of the Association for Information Science and Technology Annual Meeting (ASIST 2015), 6–10 November, St. Louis (2015)

    Google Scholar 

  29. Tschiatschek, S., Singla, A., Gomez Rodriguez, M., Merchant, A., Krause, A.: Fake news detection in social networks via crowd signals. In: Companion Proceedings of the The Web Conference 2018 (WWW 2018). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, pp. 517–524 (2018). https://doi.org/10.1145/3184558.3188722

  30. Zhang, X., Ghorbani, A.: An overview of online fake news: characterization, detection, and discussion. Inf. Process. Manag. (2019). ISSN 0306-4573, https://doi.org/10.1016/j.ipm.2019.03.004

  31. Ibrishimova, M.D.: Cyber incident classification: issues and challenges. In: Xhafa, F., Leu, F.Y., Ficco, M., Yang, C.T. (eds.) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2018. Lecture Notes on Data Engineering and Communications Technologies, vol. 24 (2018)

    Google Scholar 

  32. Ibrishimova, M.D., Li, K.F.: Automating incident classification using sentiment analysis and machine learning. In: Traore, I., Woungang, I., Ahmed, S., Malik, Y. (eds.) Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments, ISDDC 2018. Lecture Notes in Computer Science, vol. 11317 (2018)

    Google Scholar 

  33. Thorne, J., Vlachos, A., Christodoulopoulos, C., Mittal, A.: FEVER: a large-scale dataset for fact extraction and verification. In: NAACL-HLT (2018)

    Google Scholar 

  34. Hassan, N., Arslan, F., Li, C., Tremayne, M.: Toward automated fact-checking: detecting check-worthy factual claims by claimbuster. In: Proceedings of the 23rd ACM York, NY, USA, pp. 1803–1812 (2017). https://doi.org/10.1145/3097983.3098131

  35. Landauer, T.K., Foltz, P.W., Laham, D.: An introduction to latent semantic analysis. Discourse Process. 25(2–3), 259–284 (1998). https://doi.org/10.1080/01638539809545028

    Article  Google Scholar 

  36. Turney, P.D.: Measuring semantic similarity by latent relational analysis. In: Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI 2005, Edinburgh, Scotland, pp. 1136–1141 (2005)

    Google Scholar 

  37. Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using Wikipedia-based explicit semantic analysis. In: Sangal, R., Mehta, H., Bagga, R.K. (eds.) Proceedings of the 20th International Joint Conference on Artifical Intelligence (IJCAI 2007), pp. 1606–1611. Morgan Kaufmann Publishers Inc., San Francisco (2007)

    Google Scholar 

  38. Radinsky, K., Agichtein, E., Gabrilovich, E., Markovitch, S.: A word at a time: computing word relatedness using temporal semantic analysis. In: Proceedings of the 20th International Conference on World Wide Web (WWW 2011), pp. 337–346. ACM, New York (2011). http://dx.doi.org/10.1145/1963405.1963455

  39. Nguyen, T.H., Di Francesco, M., Ylä-Jääski, A.: Extracting knowledge from Wikipedia articles through distributed semantic analysis. In: Lindstaedt, S., Granitzer, M. (eds.) Proceedings of the 13th International Conference on Knowledge Management and Knowledge Technologies (i-Know 2013), Article 6, 8 p. ACM, New York (2013). https://doi.org/10.1145/2494188.2494195

  40. Google NLP API. https://cloud.google.com/natural-language/. Accessed 02 Apr 2019

  41. News API. https://newsapi.org. Accessed 02 Apr 2019

  42. Fake News Dataset. https://www.kaggle.com/c/fake-news/data. Accessed 02 Apr 2019

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, V8P5C2, Canada

    Marina Danchovsky Ibrishimova & Kin Fun Li

Authors
  1. Marina Danchovsky Ibrishimova
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kin Fun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kin Fun Li .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Division of Computer Science and Intelligent Systems, Faculty of Science and Technology, Oita University, Oita, Japan

    Hiroaki Nishino

  3. School of Science and Technology, Kwansei Gakuin University, Sanda, Japan

    Hiroyoshi Miwa

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ibrishimova, M.D., Li, K.F. (2020). A Machine Learning Approach to Fake News Detection Using Knowledge Verification and Natural Language Processing. In: Barolli, L., Nishino, H., Miwa, H. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2019. Advances in Intelligent Systems and Computing, vol 1035. Springer, Cham. https://doi.org/10.1007/978-3-030-29035-1_22

Download citation

Publish with us

Policies and ethics

Search

Navigation