DeepFakE: improving fake news detection using tensor decomposition-based deep neural network


Social media platforms have simplified the sharing of information, which includes news as well, as compared to traditional ways. The ease of access and sharing the data with the revolution in mobile technology has led to the proliferation of fake news. Fake news has the potential to manipulate public opinions and hence, may harm society. Thus, it is necessary to examine the credibility and authenticity of the news articles being shared on social media. Nowadays, the problem of fake news has gained massive attention from research communities and needed an optimal solution with high efficiency and low efficacy. Existing detection methods are based on either news-content or social-context using user-based features as an individual. In this paper, the content of the news article and the existence of echo chambers (community of social media-based users sharing the same opinions) in the social network are taken into account for fake news detection. A tensor representing social context (correlation between user profiles on social media and news articles) is formed by combining the news, user and community information. The news content is fused with the tensor, and coupled matrix-tensor factorization is employed to get a representation of both news content and social context. The proposed method has been tested on a real-world dataset: BuzzFeed. The factors obtained after decomposition have been used as features for news classification. An ensemble machine learning classifier (XGBoost) and a deep neural network model (DeepFakE) are employed for the task of classification. Our proposed model (DeepFakE) outperforms with the existing fake news detection methods by applying deep learning on combined news content and social context-based features as an echo-chamber.

This is a preview of subscription content, access via your institution.

Fig. 1

Source Facebook®

Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7


  1. 1.

    The dataset can be downloaded from:

  2. 2.

    The dataset can be downloaded from:

  3. 3.

    The dataset can be downloaded from:

  4. 4.

    The dataset can be downloaded from:

  5. 5.

    The dataset can be downloaded from:


  1. 1.

    Ghani NA, Hamid S, Hashem IAT, Ahmed E (2019) Social media big data analytics: a survey. Comput Hum Behav 101:417–428

    Article  Google Scholar 

  2. 2.

    Zhou X, Zafarani R (2018) Fake news: survey of research, detection methods, and opportunities. arXiv preprint arXiv:1812.00315

  3. 3.

    Sharma K, Qian F, Jiang H, Ruchansky N, Zhang M, Liu Y (2019) Combating fake news: a survey on identification and mitigation techniques. ACM Trans Intell Syst Technol (TIST) 10(3):21

    Google Scholar 

  4. 4.

    Shu K, Sliva A, Wang S, Tang J, Liu H (2017) Fake news detection on social media: a data mining perspective. ACM SIGKDD Explor Newsl 19(1):22–36

    Article  Google Scholar 

  5. 5.

    Persily N (2017) The 2016 US election: Can democracy survive the internet? J Democr 28(2):63–76

    Article  Google Scholar 

  6. 6.

    Ruchansky N, Seo S, Liu Y (2017) Csi: a hybrid deep model for fake news detection. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. ACM, pp 797–806

  7. 7.

    Rabanser S, Shchur O, Gnnemann S (2017) Introduction to tensor decompositions and their applications in machine learning. arXiv preprint arXiv:1711.10781

  8. 8.

    Fazil M, Abulaish M (2018) A hybrid approach for detecting automated spammers in twitter. IEEE Trans Inf Forensics Secur 13(11):2707–2719

    Article  Google Scholar 

  9. 9.

    Chong E, Han C, Park FC (2017) Deep learning net works for stock market analysis and prediction: methodology, data representations, and case studies. Expert Syst Appl 83:187–205

    Article  Google Scholar 

  10. 10.

    Ott M, Choi Y, Cardie C, Hancock JT (2011) Finding deceptive opinion spam by any stretch of the imagination. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol 1. Association for Computational Linguistics, pp 309–319

  11. 11.

    Feng S, Banerjee R, Choi Y (2012) Syntactic stylometry for deception detection. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers, vol 2. Association for Computational Linguistics, pp 171–175

  12. 12.

    Chen Y, Conroy NJ, Rubin VL (2015) Misleading online content: recognizing clickbait as false news. In: Proceedings of the 2015 ACM on Workshop on Multimodal Deception Detection. ACM, pp 15–19

  13. 13.

    Tacchini E, Ballarin G, Vedova ML. Della M, Moret S, de Alfaro L (2017) Some like it hoax: Automated fake news detection in social networks. arXiv preprint arXiv:1704.07506

  14. 14.

    Gupta M, Zhao P, Han J (2012) Evaluating event credibility on twitter. In: Proceedings of the 2012 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, pp 153–164

  15. 15.

    Shu K, Wang S, Liu H (2019) Beyond news contents: the role of social context for fake news detection. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. ACM, pp 312–320

  16. 16.

    Gupta S, Thirukovalluru R, Sinha M, Mannarswamy S (2018) CIMTDetect: a community infused matrix-tensor coupled factorization based method for fake news detection. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, pp 278–281

  17. 17.

    Ma J, Gao W, Mitra P, Kwon S, Jansen BJ, Wong K-F, Cha M (2016) Detecting rumors from microblogs with recurrent neural networks. In: IJCAI, pp 3818–3824

  18. 18.

    Yang Y, Zheng L, Zhang J, Cui Q, Li Zn, Yu PS (2018) TI-CNN: convolutional neural networks for fake news detection. arXiv preprint arXiv:1806.00749

  19. 19.

    Zhang J, Cui L, Fu Y, Gouza FB (2018) Fake news detection with deep diffusive network model. arXiv preprint arXiv:1805.08751

  20. 20.

    Zhang X, Tang Y, Wang H, Chunxiang X, Miao Y, Cheng H (2019) Lattice-based proxy-oriented identity-based encryption with keyword search for cloud storage. Inf Sci 494:193–207

    MathSciNet  Article  Google Scholar 

  21. 21.

    Zhang Q, Qiu Q, Guo W, Guo K, Xiong N (2016) A social community detection algorithm based on parallel grey label propagation. Comput Netw 107:133–143

    Article  Google Scholar 

  22. 22.

    Zhong S, Chen T, He F, Niu Y (2014) Fast Gaussian kernel learning for classification tasks based on specially structured global optimization. Neural Netw 57:51–62

    Article  Google Scholar 

  23. 23.

    Zheng X, Zeng Z, Chen Z, Yuanlong Y, Rong C (2015) Detecting spammers on social networks. Neurocomputing 159:27–34

    Article  Google Scholar 

  24. 24.

    Ibrain l, Lloret L (2019) Fake news detection using Deep Learning. arXiv preprint arXiv:1910.03496

  25. 25.

    Clauset A, Newman MEJ, Moore C (2004) Finding community structure in very large networks. Phys Rev E 70(6):066111

    Article  Google Scholar 

  26. 26.

    Torlay L, Perrone-Bertolotti M, Thomas E, Baciu M (2017) Machine learningXGBoost analysis of language networks to classify patients with epilepsy. Brain Inf 4(3):159

    Article  Google Scholar 

  27. 27.

    Acar E, Kolda TG, Dunlavy DM (2011) All-at-once optimization for coupled matrix and tensor factorizations. arXiv preprint arXiv:1105.3422

  28. 28.

    Chen T, Guestrin C (2016) Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, pp 785–794 (2016)

  29. 29.

    Harshman RA (1970) Foundations of the PARAFAC procedure: models and conditions for an explanatory multimodal factor analysis 1–84 (1970)

  30. 30.

    Lee DD, Seung HS (2001) Algorithms for non-negative matrix factorization. In: Advances in Neural Information Processing Systems, pp 556–562

  31. 31.

    Khatri CG, Rao CR (1968) Solutions to some functional equations and their applications to characterization of probability distributions. Sankhy Indian J Stat Ser A 167–180 (1968)

  32. 32.

    Moreno PJ, Logan B, Raj B (2001) A boosting approach for confidence scoring. In: Seventh European Conference on Speech Communication and Technology

  33. 33.

    Patidar R, Sharma L (2011) Credit card fraud detection using neural network. Int J Soft Comput Eng (IJSCE) 1(32–38)

  34. 34.

    Jain AK, Mao J, Mohiuddin KM (1996) Artificial neural networks: a tutorial. Computer 3:31–44

    Article  Google Scholar 

  35. 35.

    Zurada JM (1992) Introduction to artificial neural systems, vol 8. West Publishing Company, St. Paul

    Google Scholar 

  36. 36.

    Zhong B, Xing X, Love P, Wang X, Luo H (2019) Convolutional neural network: deep learning-based classification of building quality problems. Adv Eng Inform 40:46–57

    Article  Google Scholar 

  37. 37.

    Chen G, Parada C, Heigold G (2014) Small-footprint keyword spotting using deep neural networks. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp 4087–4091 (2014)

  38. 38.

    Wang Y, Ma F, Jin Z, Yuan Y, Xun G, Jha K, Su L, Gao J (2018) EANN: event adversarial neural networks for multi-modal fake news detection. In: Proceedings of the 24th ACM SIGKDDD International Conference on Knowledge Discovery & Data Mining, pp 849–857

  39. 39.

    Wu H, Gu X (2015) Max-pooling dropout for regularization of convolutional neural networks. In: International Conference on Neural Information Processing. Springer, Cham, pp 46–54

  40. 40.

    Wager S, Wang S, Liang PS (2013) Dropout training as adaptive regularization. In: Advances in Neural Information Processing Systems, pp 351–359

  41. 41.

    Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958

    MathSciNet  MATH  Google Scholar 

  42. 42.

    Vasudevan V, Zoph B, Shlens J, Le QV (2019) Neural architecture search for convolutional neural networks. U.S. Patent Application 16/040,067, filed January 24 (2019)

  43. 43.

    Li Y, Yuan Y (2017) Convergence analysis of two-layer neural networks with relu activation. In: Advances in Neural Information Processing Systems, pp 597–607 (2017)

  44. 44.

    He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1026–1034

  45. 45.

    Natekin A, Knoll A (2013) Gradient boosting machines, a tutorial. Front Neurorobotics 7:21

    Article  Google Scholar 

  46. 46.

    Shu K, Mahudeswaran D, Wang S, Lee D, Liu H (2018) Fakenewsnet: a data repository with news content, social context and dynamic information for studying fake news on social media. arXiv preprint arXiv:1809.01286

  47. 47.

    Papanastasiou F, Katsimpras G, Paliouras G (2019) Tensor factorization with label information for fake news detection. arXiv preprint arXiv:1908.03957

  48. 48.

    Maciej S (2019) Fakenewscorpus Online:, Accessed 29 Mar 2019

Download references

Author information



Corresponding author

Correspondence to Pratik Narang.

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

Verify currency and authenticity via CrossMark

Cite this article

Kaliyar, R.K., Goswami, A. & Narang, P. DeepFakE: improving fake news detection using tensor decomposition-based deep neural network. J Supercomput 77, 1015–1037 (2021).

Download citation


  • Social media
  • Fake news
  • Deep learning
  • Echo chamber
  • Tensor factorization