Abstract
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.
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Notes
The dataset can be downloaded from: https://twitter.com/PolitiFact.
The dataset can be downloaded from: https://www.researchgate.net/figure/Dataset-from-Sina-Weibo_tbl1_282028558.
The dataset can be downloaded from: https://www.kaggle.com/mrisdal/fake-news.
The dataset can be downloaded from: https://www.kaggle.com/c/fake-news/data.
The dataset can be downloaded from: https://www.kaggle.com/mdepak/fakenewsnet.
References
Ghani NA, Hamid S, Hashem IAT, Ahmed E (2019) Social media big data analytics: a survey. Comput Hum Behav 101:417–428
Zhou X, Zafarani R (2018) Fake news: survey of research, detection methods, and opportunities. arXiv preprint arXiv:1812.00315
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
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
Persily N (2017) The 2016 US election: Can democracy survive the internet? J Democr 28(2):63–76
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
Rabanser S, Shchur O, Gnnemann S (2017) Introduction to tensor decompositions and their applications in machine learning. arXiv preprint arXiv:1711.10781
Fazil M, Abulaish M (2018) A hybrid approach for detecting automated spammers in twitter. IEEE Trans Inf Forensics Secur 13(11):2707–2719
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
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
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
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
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
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
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
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
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
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
Zhang J, Cui L, Fu Y, Gouza FB (2018) Fake news detection with deep diffusive network model. arXiv preprint arXiv:1805.08751
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
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
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
Zheng X, Zeng Z, Chen Z, Yuanlong Y, Rong C (2015) Detecting spammers on social networks. Neurocomputing 159:27–34
Ibrain l, Lloret L (2019) Fake news detection using Deep Learning. arXiv preprint arXiv:1910.03496
Clauset A, Newman MEJ, Moore C (2004) Finding community structure in very large networks. Phys Rev E 70(6):066111
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
Acar E, Kolda TG, Dunlavy DM (2011) All-at-once optimization for coupled matrix and tensor factorizations. arXiv preprint arXiv:1105.3422
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)
Harshman RA (1970) Foundations of the PARAFAC procedure: models and conditions for an explanatory multimodal factor analysis 1–84 (1970)
Lee DD, Seung HS (2001) Algorithms for non-negative matrix factorization. In: Advances in Neural Information Processing Systems, pp 556–562
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)
Moreno PJ, Logan B, Raj B (2001) A boosting approach for confidence scoring. In: Seventh European Conference on Speech Communication and Technology
Patidar R, Sharma L (2011) Credit card fraud detection using neural network. Int J Soft Comput Eng (IJSCE) 1(32–38)
Jain AK, Mao J, Mohiuddin KM (1996) Artificial neural networks: a tutorial. Computer 3:31–44
Zurada JM (1992) Introduction to artificial neural systems, vol 8. West Publishing Company, St. Paul
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
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)
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
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
Wager S, Wang S, Liang PS (2013) Dropout training as adaptive regularization. In: Advances in Neural Information Processing Systems, pp 351–359
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
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)
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)
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
Natekin A, Knoll A (2013) Gradient boosting machines, a tutorial. Front Neurorobotics 7:21
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
Papanastasiou F, Katsimpras G, Paliouras G (2019) Tensor factorization with label information for fake news detection. arXiv preprint arXiv:1908.03957
Maciej S (2019) Fakenewscorpus Online: https://github.com/several27/FakeNewsCorpus, Accessed 29 Mar 2019
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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). https://doi.org/10.1007/s11227-020-03294-y
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DOI: https://doi.org/10.1007/s11227-020-03294-y