Skip to main content
SpringerLink
Log in

An Attribute-wise Attention model with BiLSTM for an efficient Fake News Detection

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

Abstract

Nowadays, fake news propaganda is the main threat to society, as it has the potential to misdirect public behaviour and provoke violence and extremism. Recently, to prevent the spread of misinformation, researchers have devised various fake news detection models with deep learning techniques. This research aims to develop a unique deep architecture to capture the basic insights about the document in the form of a summarised feature vector representation for better fake news detection. The proposed model, namely AA-BiLSTM includes Bi-directional Long Short-Term Memory (Bi-LSTM) and an attribute-wise attention mechanism based on Convolutional Neural Network (CNN) as the CNN models extract higher-level features using convolutional layers and average pooling layers. Moreover, this model suitably pays attention to the word or sentence based on the dependent attribute by applying the attention mechanism after the fusing process. The performance of the proposed AA-BiLSTM has been experimentally evaluated on four benchmark datasets, namely Kaggle fake_real_news [2022], Kaggle fake_real_news [2016], ISOT and Liar datasets, and compared with the existing state-of-the-art fake news detection methods. For the Kaggle fake_real_news 2016 and ISOT fake news datasets, the AA-BiLSTM algorithm achieved an accuracy of more than 99%. In the Liar dataset, the AA-BiLSTM method surpassed all basic models with an accuracy of 60.31%.

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

Similar content being viewed by others

Data Availability

The datasets analyzed during the current study are available in public repositories.

1. https://www.kaggle.com/datasets/jillanisofttech/fake-or-real-news.

2. fakenews.mit.edu

3. https://www.uvic.ca/ecs/ece/isot/datasets/fake-news/index.php

4. https://huggingface.co/datasets/liar

References

  1. Singhania S, Fernandez N, Rao S (2017) 3HAN: A deep neural network for fake news detection. In: Liu D, Xie S, Li Y, Zhao D, El-Alfy ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science, vol 10635. Springer, Cham. https://doi.org/10.1007/978-3-319-70096-0_59

  2. 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 

  3. Rodríguez ÁI, Iglesias LL (2019) Fake news detection using Deep Learning. arXiv preprint arXiv:1910.03496

  4. Monti F, Frasca F, Eynard D, Mannion D, Bronstein MM (2019) Fake news detection on social media using geometric deep learning. arXiv preprint arXiv:1902.06673

  5. Nguyen DM, Do TH, Calderbank R, Deligiannis N (2019) Fake news detection using deep markov random fields. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1391–1400

  6. Abedalla A, Al-Sadi A, Abdullah M (2019) A closer look at fake news detection: A deep learning perspective. In Proceedings of the 2019 3rd International Conference on Advances in Artificial Intelligence, pp. 24–28

  7. 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

  8. Kumar S, Asthana R, Upadhyay S, Upreti N, Akbar M (2020) Fake news detection using deep learning models: A novel approach. Trans Emerging Tel Tech 31:e3767

    Article  Google Scholar 

  9. Sahoo SR, Gupta BB (2021) Multiple features-based approach for automatic fake news detection on social networks using deep learning. Appl Soft Comput 100:106983

    Article  Google Scholar 

  10. Trueman TE, Ashok Kumar J, Narayanasamy P, Vidya J (2021) Attention-based C-BiLSTM for fake news detection. Appl Soft Comput 110. https://doi.org/10.1016/j.asoc.2021.107600

  11. Sridhar S, Sanagavarapu S (2021) Fake News Detection and Analysis using Multitask Learning with BiLSTM CapsNet model, 2021 11th International Conference on Cloud Computing, Data Science & Engineering(Confluence), pp. 905–911, doi: 10.1109/ Confluence 51648.2021.9377080

  12. Shu K, Sliva A, Wang S, Tang J, Liu H (2017) Fake news detection on social media: A data mining perspective. Association for Computing Machinery 19, issn=1931–0145. https://doi.org/10.1145/3137597.3137600

  13. Elhadad MK, Fun Li K, Gebali F (2019) Fake news detection on social media: A systematic survey, 2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM), pp. 1–8. https://doi.org/10.1109/PACRIM47961.2019.8985062

  14. Wani A, Joshi I, Khandve S, Wagh V, Joshi R (2021) Evaluating deep learning approaches for Covid19 fake news detection. In: Chakraborty T, Shu K, Bernard HR, Liu H, Akhtar MS (eds) Combating Online Hostile Posts in Regional Languages during Emergency Situation. CONSTRAINT 2021. Communications in Computer and Information Science, vol 1402. Springer, Cham

  15. Yang S, Shu K, Wang S, Gu R, Wu F, Liu H (2019) Unsupervised Fake News Detection on Social Media: A Generative Approach. Proc AAAI Conf Artif Intell 33(01):5644–5651

    Google Scholar 

  16. Pennington J, Socher R, Manning CD (2014) GloVe: Global Vectors for Word Representation, In Proceedings of the conference on empirical methods in natural language processing (EMNLP), pp. 1532–1543

  17. Rezaeinia SM, Ghodsi A, Rahmani R (2017) Improving the accuracy of pre-trained word embeddings for sentiment analysis. Comput Language, ArXiv abs/1711.08609. https://doi.org/10.48550/arXiv.1711.08609

  18. GloVe: Global Vectors for Word Representation, https://nlp.stanford.edu/projects/glove/, last accessed 2019/07/13

  19. Brownlee (J) How to Develop a Bidirectional LSTM For Sequence Classification in Python with Keras, Machine Learning Mastery, 17-Jan-2021. [Online]. Available: https://machinelearningmastery.com/develop-bidirectional-lstm-sequence-classification-python-keras/. Accessed: 29 May 2021

  20. Palash Sharma P (2020) Keras dense layer explained for beginners. MLK - Machine Learning Knowledge. https://machinelearningknowledge.ai/keras-dense-layer-explained-for-beginners/. Accessed 12 Apr 2021

  21. Zhao Z, Wu Y (2016) Attention-based convolutional neural networks for sentence classification. Interspeech 8:705–709. https://doi.org/10.21437/Interspeech.2016-354

  22. Fang Y, Gao J, Huang C, Peng H, Runpu Wu (2019) Self multi-head attention-based convolutional neural networks for fake news detection. PLoS ONE 14(9):e0222713

    Article  Google Scholar 

  23. Yang Z, Yang D, Dyer C, He X, Smola A, Hovy E (2016) Hierarchical Attention Networks for Document Classification, Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics, 1480–1489

  24. Tech JS, Shetgaonkar A, Sasaki T. Fake or real news, Kaggle, 31-Mar-2022. [Online]. Available: https://www.kaggle.com/datasets/jillanisofttech/fake-or-real-news. Accessed 20 Apr 2022

  25. Risdal M (2016) “Getting real about fake news”. Kaggle. https://www.kaggle.com/datasets/mrisdal/fake-news. https://doi.org/10.34740/KAGGLE/DSV/911

  26. Wang W (2017) “Liar, Liar Pants on Fire”: A New Benchmark Dataset for Fake News Detection. 422–426. https://doi.org/10.18653/v1/P17-2067

  27. Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473

  28. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  29. Lin Z, Feng M, Santos CNd, Yu M, Xiang B, Zhou B et al (2017) A structured self-attentive sentence embedding. arXiv preprint arXiv:170303130

  30. Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:14123555. Available from: https://arxiv.org/abs/1412. 3555

  31. Alameri SA, Mohd M (2021) Comparison of fake news detection using machine learning and deep learning techniques, 2021 3rd International Cyber Resilience Conference (CRC), pp. 1–6

  32. Wang WY (2017) Dataset for Fake News Detection, Association for Computational Linguistics, Stroudsburg, PA, USA

  33. Kim Y (2014) Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, Doha, Qatar, p 1746–1751

  34. Yang Wang W, Yang D (2015) That’s so annoying!!!: A lexical and frame-semantic embedding based data augmentation approach to automatic categorization of annoying behaviors using #petpeeve tweets. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP 2015). ACL, Lisbon, Portugal

  35. Graves A, Schmidhuber J (2005) Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw 18(5–6):602–610. https://doi.org/10.1016/j.neunet.2005.06.042

Download references

Acknowledgements

The authors are very grateful to the family members and research committee for their constant support and to the editors and reviewers for their valuable and prolific suggestions to refine the quality of the paper.

Author information

Authors and Affiliations

  1. Sarah Tucker College (Affiliated to Manonmaniam Sundaranar University, Tirunelveli), Tirunelveli, Tamil Nadu, India

    Adline Rajasenah Merryton

  2. Kamaraj College (Affiliated to Manonmaniam Sundaranar University, Tirunelveli), Thoothukudi, Tamil Nadu, India

    M. Gethsiyal Augasta

Authors
  1. Adline Rajasenah Merryton
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Gethsiyal Augasta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Gethsiyal Augasta.

Ethics declarations

Declarations

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

Conflicts of interest/Competing interests

The authors declare that they have no competing interests.

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

Merryton, A.R., Gethsiyal Augasta, M. An Attribute-wise Attention model with BiLSTM for an efficient Fake News Detection. Multimed Tools Appl 83, 38109–38126 (2024). https://doi.org/10.1007/s11042-023-16824-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-16824-6

Keywords

Search

Navigation