Skip to main content
SpringerLink
Log in

Fake news detection and classification using hybrid BiLSTM and self-attention model

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

Abstract

Living in the twenty-first century, from shopping to reading news articles everything has changed, everything has become online. Anyone can access most of everything with a single touch from a cell phone. Internet is the new normal, everyone is very much attached to it. Reading news online is something very common among people of all age groups, thousands of articles are being published on various online media portals online every hour. These articles are not necessarily genuine always, sometimes false information is written knowingly and sometimes knowingly. It is very much needed to keep these articles away from the users. Many kinds of research have been conducted using traditional mathematical models and sequential neural networks to detect this fraud news online. In most of these studies, the news is being analysed in a unidirectional way. Therefore, a need of changing current mechanisms is required to increases the accuracy of false news detection. In this paper, we propose a Bi-LSTM based (Bidirectional long short term memory) deep learning approach by adding self-attention on top of it. This helps in developing a higher clarity, which is the most challenging part of the deep learning paradigm. The classification result demonstrated that the proposed hybrid deep learning model outperforms existing models with an accuracy score of 98.65%.

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

Access this article

Log in via an institution

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

Data availability

NA

Notes

  1. https://www.kaggle.com/c/fake-news/data

References

  1. Agarwal A Dixit A (2020) Fake news detection: an ensemble learning approach. 4th international conference on intelligent computing and control systems (ICICCS), pp. 1178-1183

  2. Ahmed H, Traore I, Saad S (2017) Detection of online fake news using n-gram analysis and machine learning techniques, In: International conference on intelligent, secure, and dependable systems in distributed and cloud environments, Springer, pp. 127–138

  3. Ajao O, Bhowmik D, Zargari S (2018) Fake news identification on twitter with hybrid CNN and RNN models. SM society '18: proceedings of the 9th international conference on social media and society, Pages 226–230 https://doi.org/10.1145/3217804.3217917

  4. Allcott H, Gentzkow M (2017) Social media and fake news in the 2016 election. J Econ Perspect 31(2):211–236

    Article  Google Scholar 

  5. Al-Zaman MS (2021) COVID-19-related social media fake news in India. J Med 2:100–114

    Google Scholar 

  6. Asparouhov T, Muthen B (2010) Weighted least squares estimation with missing data. Published to Mplus Technical Appendix 2010: 1–10

  7. Berinsky A (2017) Rumours and health care reform: experiments in political misinformation. Br J Polit Sci 47(2):241–262

    Article  Google Scholar 

  8. Bhutani B, Rastogi N, Sehgal P, Purwar A (2019) Fake News Detection Using Sentiment Analysis. 2019 Twelfth International Conference on Contemporary Computing (IC3), pp. 1–5. https://doi.org/10.1109/IC3.2019.8844880

  9. Castillo C, Mendoza M, Poblete B (2011) Information credibility on twitter. In: Proceedings of the 20th international conference on world wide web, pp 675–684

  10. Celliers M., Hattingh M. (2020) A Systematic Review on Fake News Themes Reported in Literature. In: Proceedings of the conference on e-Business, e-Services and e-Society I3E 2020: Responsible Design, Implementation and Use of Information and Communication Technology pp 223–234

  11. Cerisara C, Kral P, Lenc L (2018) On the effects of using word2vec representations in neural networks for dialogue act recognition. Comput Speech Lang 47:175–193

    Article  Google Scholar 

  12. Conroy NJ, Rubin VL, Chen Y (2015) Automatic deception detection: methods for finding fake news. Proc Assoc Inf Sci Technol 52(1):1–4

    Article  Google Scholar 

  13. Del Vicario M, Bessi A, Zollo F, Petroni F, Scala A, Caldarelli G, Eugene Stanley H, Quattrociocchi W (2016) The spreading of misinformation online. Proc Natl Acad Sci 113(3):554–559

    Article  Google Scholar 

  14. Girgis S, Amer E, Gadallah M (2018) Deep Learning Algorithms for Detecting Fake News in Online Text. 2018 13th International Conference on Computer Engineering and Systems (ICCES), pp. 93–97, https://doi.org/10.1109/ICCES.2018.8639198

  15. Gorrell G, Kochkina E, Liakata M, Aker A, Zubiaga A, Bontcheva K, Derczynski L (2019) SemEval2019 task 7:” RumourEval, determining rumour veracity and support for rumours.” In: Proceedings of the 13th International Workshop on Semantic Evaluation, pp. 845–854

  16. Gundapu S, Mamidi R (21 Jan 2021) Transformer based Automatic COVID-19 Fake News Detection System ” arXiv:2101.00180v3 [cs.CL]

  17. Hiramath CK, Deshpande GC (2019) Fake News Detection Using Deep Learning Techniques. 2019 1st International Conference on Advances in Information Technology (ICAIT), pp. 411–415. https://doi.org/10.1109/ICAIT47043.2019.8987258

  18. Islam MR, Liu S, Wang X, Xu G (2020) Deep learning for misinformation detection on online social networks: a survey and new perspectives. Soc Netw Anal Min 10:Article number: 82

  19. Kaliyar RK (2018) Fake News Detection Using A Deep Neural Network," 2018 4th International Conference on Computing Communication and Automation (ICCCA), pp. 1–7. https://doi.org/10.1109/CCAA.2018.8777343

  20. Kulkarni P, Karwande S, Keskar R, Kale P, Iyer S (2021) Fake news detection using machine learning. ITM Web Conf 40:030003. https://doi.org/10.1051/itmconf/20214003003

    Article  Google Scholar 

  21. Kumar S, Shah N (23 Apr 2018) False Information on Web and Social Media: A Survey. arXiv:1804.08559v1 [cs.SI]

  22. Kyriakides G, Margaritis K (2019) An Introduction to Neural Architecture Search for Convolutional Networks. issued December 31 arXiv:2005.11074

  23. Lazer D, Baum M, Benkler Y, Berinsky A, Greenhill K, Menczer F, Metzger M, Nyhan B, Pennycook G, Rothschild D, Schudson M, Sloman S, Sunstein C, Thorson E, Watts D, Zittrain J (2018) The science of fake news. Science.” Published to Science 09 Mar 2018 359(6380):1094–1096

    Article  Google Scholar 

  24. Li Y, Yang Y (2017) Convergence analysis of two-layer neural networks with relu activation. 31st conference on neural information processing systems (NIPS 2017), Long Beach, CA, USA, pp. 597–607

  25. Mo Jang S, Kim JK (2018) Third person effects of fake news: Fake news regulation and media literacy interventions. Comput Hum Behav 80:295–302. https://doi.org/10.1016/j.chb.2017.11.034

    Article  Google Scholar 

  26. Nasir JA, Khan OS, Varlamis I (2021) Fake news detection: A hybrid CNN-RNN based deep learning approach. Int J Inf Manag Data Insights 1(1):100007, ISSN 2667–0968. https://doi.org/10.1016/j.jjimei.2020.100007

    Article  Google Scholar 

  27. Nida A. Irfan UK, Farah Salem A, Lama Abdulaziz A, Asma Khaled A (2021) Fake detect: A deep learning ensemble model for fake news detection, Vol 2021, 5557784, https://doi.org/10.1155/2021/5557784

  28. Pennycook G, Rand DG (2020) Who falls for fake news? The roles of bullshit receptivity, overclaiming, familiarity, and analytic thinking. J Pers 88:185–200. https://doi.org/10.1111/jopy.12476

    Article  Google Scholar 

  29. Qawasmeh E, Tawalbeh M, Abdullah M (2019) Automatic Identification of Fake News Using Deep Learning. 2019 Sixth international conference on social networks analysis, Management and Security (SNAMS), pp. 383–388, https://doi.org/10.1109/SNAMS.2019.8931873

  30. Qi Y, Sachan D, Felix M, Padmanabhan S, Neubig G (2018) When and why are pre-trained word embeddings useful for neural machine translation?. In: proceedings of the 2018 conference of the north American chapter of the association for computational linguistics: human language technologies, vol 2 (short papers), pp 529–535

  31. Reddy P, Roy D, Manoj P, Keerthana M, Tijare P (2019) A Study on Fake News Detection Using Naïve Bayes, SVM, Neural Networks and LSTM. J Adv Res Dyn Control Syst 1:942–947

  32. Saikh T, De A, Ekbal A, Bhattacharyya P (2019) A deep learning approach for automatic detection of fake news. In: Proceedings of the 16th International Conference on Natural Language Processing, International Institute of Information Technology, Hyderabad, pp. 230–238

  33. Seide F, Li G, Chen X, Yu D (2011) Feature engineering in context-dependent deep neural networks for conversational speech transcription. 2011 IEEE Workshop on Automatic Speech Recognition & Understanding, 24–29, https://doi.org/10.1109/ASRU.2011.6163899

  34. Singh LK, Pooja GH, Khanna M, Bhadoria RS (2021) An enhanced deep image model for glaucoma diagnosis using feature-based detection in retinal fundus. Med Biol Eng Comput 59(2):333–353

    Article  Google Scholar 

  35. Vosoughi S, Neo Mohsenvand M, Roy D (2017) Rumour gauge: predicting the veracity of rumours on twitter. ACM Trans Knowl Discov Data 11(4):1–36

    Article  Google Scholar 

  36. Young T, Hazarika D, Poria S, Cambria E (Aug. 2018) Recent trends in deep learning based natural language processing [review article]. IEEE Comput Intell Mag 13(3):55–75. https://doi.org/10.1109/MCI.2018.2840738

    Article  Google Scholar 

  37. Zhou X, Zafarani R (2018) A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities. ACM Comput Surv 53(5) October 2020 Article No.: 109 pp 1–40. https://doi.org/10.1145/3395046

Download references

Code availability

Custom Code.

Author information

Authors and Affiliations

  1. School of Electronics Engineering, Vellore Institute of Technology, Vellore, India

    Asutosh Mohapatra, Nithin Thota & P. Prakasam

Authors
  1. Asutosh Mohapatra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nithin Thota
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. P. Prakasam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P. Prakasam.

Ethics declarations

Conflict of interests

The authors declare that there is no conflict of interest regarding the publication of this paper and that the work presented in this article is not supported by any funding agency.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mohapatra, A., Thota, N. & Prakasam, P. Fake news detection and classification using hybrid BiLSTM and self-attention model. Multimed Tools Appl 81, 18503–18519 (2022). https://doi.org/10.1007/s11042-022-12764-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-12764-9

Keywords

Search

Navigation