[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

A fusion of BERT, machine learning and manual approach for fake news detection

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

A large number of users around the globe have preferred to read news and the latest information from the Internet, especially social media, leaving behind the traditional approach of print media. On the one hand, the Internet is a constructive medium to spread the latest news and information briefly. On the other hand, malicious users are very active on the Internet and spread fake news, which becomes viral within a few minutes. The spread of fake news has become a serious threat as many users now rely on Internet news without verification. In this digital world, it is easy to spread any toxic information over the Internet, like hate speech, extremism, propaganda, and political agendas. It is a big challenge in today’s digital world to mitigate the spread of fake news; hence, there is a need for an automatic computational tool that can assist in measuring the credibility of news. This study aims to deliver a solution where fake news from Twitter and website-based articles can be detected using the Natural Language Processing (NLP) technique, Bidirectional Encoder Representations from Transformers (BERT), other machine learning classification algorithms, and manual program-based approaches. A dataset with fake and real labels for the textual content is used. Different classification algorithms are evaluated to find a suitable algorithm for delivering a fake news detector. The evaluations are based on machine learning and a program-based approach. The textual content that the user provides, such as an article or tweet, can confirm the legitimacy of fake news. This website offers fake news detection for both website-based news articles and tweets from Twitter in English, Arabic, and Urdu.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data availability

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. Byers K (2021) How many blogs are there? (And 141 Other Blogging Stats). Growth Badger. [Online]. Available: https://growthbadger.com/blogstats. Accessed 30 12 2021

  2. Silk J (2020) Spread of coronavirus fake news causes hundreds of deaths. DW. [Online]. Available: https://www.dw.com/en/coronavirusmisinformation/a-54529310. Accessed 30 12 2021

  3. Bovet A, Makse HA (2019) Influence of fake news in Twitter during the 2016 US presidential election. Nat Commun 10(1):1–14

    Article  ADS  Google Scholar 

  4. Menon AHS (2020) he dead professor and the vast pro-India disinformation campaign, BBC. [Online]. Available: https://www.bbc.com/news/world-asia-india-55232432. Accessed 30 12 2021

  5. Fake News. Lies and Propaganda: How to Sort Fact from Fiction,” M LIbraray, 2021 10 26. [Online]. Available: https://guides.lib.umich.edu/fakenews. Accessed 30 12 2021

  6. B. UNIVERSITY, Fake News: Develop Your Fact-Checking Skills: What Kinds of Fake News Exist?,” Benedictine University, 1 7 2021. [Online]. Available: https://researchguides.ben.edu/c.php?g=608230p=4219633. Accessed 30 12 2021

  7. Thota A, Tilak P, Ahluwalia S, Lohia N (2018) Fake news detection: a deep learning approach. SMU Data Sci Rev 1(3):10

    Google Scholar 

  8. Acharya A, Jathan A, Anchan D, Dinesh Kottary K, Mr Sunil BN (2019) Fake news detection using machine learning

  9. Ibrishimova MD, Li KF (2019) A machine learning approach to fake news detection using knowledge verification and natural language processing. In International Conference on Intelligent Networking and Collaborative Systems, pp. 223–234. Springer, Cham

  10. Yazdi KM, Yazdi AM, Khodayi S, Hou J, Zhou W, Saedy S (2020) Improving fake news detection using k-means and support vector machine approaches. Int J Electron Commun Eng 14(2):38–42

    Google Scholar 

  11. Bharadwaj P, Shao Z (2019) Fake news detection with seman- tic features and text mining. Int J Nat Lang Comput (IJNLC) 8

  12. Soni VD (2018) Prediction of geniunity of news using advanced machine learning and natural language processing algorithms. Int J Innov Res Sci Eng Technol 7(5):6349–6354

    Google Scholar 

  13. Shu K, Mahudeswaran D, Liu H (2019) FakeNewsTracker: a tool for fake news collection, detection, and visualization. Comput Math Organ Theory 25(1):60–71

    Article  Google Scholar 

  14. Gurav S, Sase S, Shinde S, Wabale P, Hirve S (2019) Survey on automated system for fake news detection using NLP machine learning approach. Int Res J Eng Technol (IRJET) 6(01):308–309

    Google Scholar 

  15. Poddar K, Umadevi KS (2019) Comparison of various machine learning models for accurate detection of fake news. In 2019 Innovations in Power and Advanced Computing Technologies (i-PACT), vol. 1, pp 1–5. IEEE

  16. Islam MU, Hossain Md M, Kashem MA (2021) COVFake: A word embedding coupled with LSTM approach for COVID related fake news detection. Int J Comput Appl 975:8887

    Google Scholar 

  17. Khanam Z, Alwasel BN, Sirafi H, Rashid M (2021) Fake news detection using machine learning approaches. IOP Conf Ser Mater Sci Eng 1099(1):012040 (IOP Publishing)

    Article  Google Scholar 

  18. Ahmed S, Hinkelmann K, Corradini F (2022) Development of fake news model using machine learning through natural language processing. arXiv preprint arXiv:2201.07489

  19. Pérez-Rosas V, Kleinberg B, Lefevre A, Mihalcea R (2017) Automatic detection of fake news. arXiv preprint arXiv:1708.07104

  20. Swapna Y, Saiprasad P, Vathsalya B, Chandrakanth S (2019) Fake News Detection using Naïve Bayes Classifier

  21. Zhang X, Cao J, Li X, Sheng Q, Zhong L, Shu K (2021) Mining dual emotion for fake news detection. Proc Web Con 2021:3465–3476

    Google Scholar 

  22. Khurana U, Bachelor Opleiding Kunstmatige Intelligentie (2017) The linguistic features of fake news headlines and statements. Diss. Master’s thesis, University of Amsterdam

  23. Aldwairi M, Alwahedi A (2018) Detecting fake news in social media networks. Procedia Computer Science 141:215–222

    Article  Google Scholar 

Download references

Acknowledgements

The authors extend their appreciation to the Deputyship for Research Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number:IFP22UQU4250002DSR230

Author information

Author notes

  1. Mohammed A. Al Ghamdi, Muhammad Shahid Bhatti, Atif Saeed, Zeeshan Gillani and Sultan H. Almotiri contributed equally to this work.

Authors and Affiliations

  1. Computer Science Department, Umm Al-Qura University, Makkah, Saudi Arabia

    Mohammed A. Al Ghamdi & Sultan H. Almotiri

  2. Department of CS, COMSATS University Islamabad Lahore, Lahore, 54600, Pakistan

    Muhammad Shahid Bhatti, Atif Saeed & Zeeshan Gillani

Authors
  1. Mohammed A. Al Ghamdi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Muhammad Shahid Bhatti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Atif Saeed
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zeeshan Gillani
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sultan H. Almotiri
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Atif Saeed, Muhammad Shahid Bhatti, Mohammed A. Al Ghamdi is the main author of the current paper. They contributed to the development of the ideas, design of the study, theory, result analysis, and paper writing. Zeeshan Gillani, Sultan H. Almotiri contributed to the result analysis and paper revision. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Muhammad Shahid Bhatti.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Al Ghamdi, M.A., Bhatti, M.S., Saeed, A. et al. A fusion of BERT, machine learning and manual approach for fake news detection. Multimed Tools Appl 83, 30095–30112 (2024). https://doi.org/10.1007/s11042-023-16669-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-16669-z

Keywords

Search

Navigation