Skip to main content

Advertisement

Springer Nature Link
Log in

Mul-FaD: attention based detection of multiLingual fake news

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

The latest buzzword in today’s world is fake news. The circulation of false information influences elections, public health, brand reputations, and violence. Hence, the severity of the threat of fake news is increasing. The danger for fake news exists everywhere globally and is not specific to one language or nation. The creators of fake news layer the facts in the news with misinformation to confuse the readers. Hence, a need arises for creating a model for detecting fake news in multiple languages. This paper proposes a unified attention-based model Mul-FaD to detect fake news in various languages. We have created our dataset with around 40000 articles in English, German, and French. This paper also shows an exploratory analysis of the dataset created. In this paper, we perform experiments from a multilingual perspective in which we use an altered hierarchical attention-based network to detect fake news. Our model is able to achieve an accuracy of 93.73 and an F1 score of 92.9 for the combined corpus of the three languages.

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
$34.99 /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 excludes VAT (USA)
Tax calculation will be finalised during checkout.

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data Availability Statement

Data available on request from the authors.

Notes

  1. https://www.forbes.com/sites/petersuciu/2019/10/11/more-americans-are-getting-their-news-from-social-media/?sh=7246217e3e17.

  2. https://www.statista.com/chart/15355/social-media-users.

  3. www.politifact.com.

  4. www.snopes.com.

  5. https://reporterslab.org/fact-checking/.

  6. https://www.thequint.com/topic/coronavirus-fact-check.

  7. https://fullfact.org/.

  8. https://www.kaggle.com/jruvika/fake-news-detection.

  9. https://www.kaggle.com/mrisdal/fake-news.

  10. https://www.kaggle.com/hassanamin/textdb3.

  11. https://www.kaggle.com/ksaivenketpatro/fake-news-detection-dataset.

  12. https://www.kaggle.com/surekharamireddy/fake-news-detection.

  13. https://fasttext.cc/.

  14. https://github.com/DebanjanaKar/Covid19_FakeNews_Detection.

  15. http://vectors.nlpl.eu/repository/.

References

  • Abonizio HQ, de Morais JI, Tavares GM, Barbon Junior S (2020) Language-independent fake news detection: English, portuguese, and spanish mutual features. Future Internet 12(5):87

    Article  Google Scholar 

  • Ahuja N, Kumar S (2020) S-han: Hierarchical attention networks with stacked gated recurrent unit for fake news detection. 2020 8th International Conference on Reliability. Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO), IEEE, pp 873–877

    Google Scholar 

  • Guibon G, Ermakova L, Seffih H, Firsov A, Le Noé-Bienvenu G (2019) Multilingual fake news detection with satire. In: CICLing: International Conference on Computational Linguistics and Intelligent Text Processing

  • He T, Huang W, Qiao Y, Yao J (2016) Text-attentional convolutional neural network for scene text detection. IEEE trans image process 25(6):2529–2541

    Article  MathSciNet  MATH  Google Scholar 

  • Horne B, Adali S (2017) This just in: Fake news packs a lot in title, uses simpler, repetitive content in text body, more similar to satire than real news. In: Proceedings of the International AAAI Conference on Web and Social Media, vol 11

  • Kar D, Bhardwaj M, Samanta S, Azad AP (2020) No rumours please! a multi-indic-lingual approach for covid fake-tweet detection. arXiv preprint arXiv:2010.06906

  • Koloski B, Pollak S, Skrlj B (2020) Multilingual detection of fake news spreaders via sparse matrix factorization. In: CLEF (Working Notes)

  • Li Y, Jiang B, Shu K, Liu H (2020) Toward a multilingual and multimodal data repository for covid-19 disinformation. In: 2020 IEEE International Conference on Big Data (Big Data), IEEE, pp 4325–4330

  • Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems. MIT Press, Cambridge, pp 3111–3119

    Google Scholar 

  • Monteiro RA, Santos RL, Pardo TA, De Almeida TA, Ruiz EE, Vale OA (2018) Contributions to the study of fake news in portuguese: New corpus and automatic detection results. International Conference on Computational Processing of the Portuguese Language. Springer, Berlin, pp 324–334

    Chapter  Google Scholar 

  • Posadas-Durán JP, Gómez-Adorno H, Sidorov G, Escobar JJM (2019) Detection of fake news in a new corpus for the spanish language. J Intell Fuzzy Syst 36(5):4869–4876

    Article  Google Scholar 

  • Qazi U, Imran M, Ofli F (2020) Geocov19: a dataset of hundreds of millions of multilingual covid-19 tweets with location information. SIGSPATIAL Special 12(1):6–15

    Article  Google Scholar 

  • Ramos J (2003) Using tf-idf to determine word relevance in document queries. Proceed first instr conf mach learn 242(1):29–48

    Google Scholar 

  • Schwarz S, Theóphilo A, Rocha A (2020) Emet: Embeddings from multilingual-encoder transformer for fake news detection. ICASSP 2020–2020 IEEE International Conference on Acoustics. Speech and Signal Processing (ICASSP), IEEE, pp 2777–2781

    Google Scholar 

  • Shahi GK, Nandini D (2020) Fakecovid–a multilingual cross-domain fact check news dataset for covid-19. arXiv preprint arXiv:2006.11343

  • Shao C, Ciampaglia GL, Varol O, Yang KC, Flammini A, Menczer F (2018) The spread of low-credibility content by social bots. Nat commun 9(1):1–9

    Article  Google Scholar 

  • Van der Maaten L, Hinton G (2008) Visualizing data using t-sne. Journal of machine learning research 9(11)

  • Varshney D, Vishwakarma DK (2020) Hoax news-inspector: a real-time prediction of fake news using content resemblance over web search results for authenticating the credibility of news articles. J Ambient Intell Humaniz Comput 896:1–14

    Google Scholar 

  • Vogel I, Meghana M (2020) Detecting fake news spreaders on twitter from a multilingual perspective. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp 599–606

  • Yang Z, Yang D, Dyer C, He X, Smola A, Hovy E (2016) Hierarchical attention networks for document classification. In: Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies, pp 1480–1489

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Delhi Technological University, Delhi, India

    Nishtha Ahuja & Shailender Kumar

Authors
  1. Nishtha Ahuja
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Shailender Kumar
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Shailender Kumar.

Ethics declarations

Conflict of interest

The authors of this paper 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

Ahuja, N., Kumar, S. Mul-FaD: attention based detection of multiLingual fake news. J Ambient Intell Human Comput 14, 2481–2491 (2023). https://doi.org/10.1007/s12652-022-04499-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-022-04499-0

Keywords