Abstract
The primary objective of this paper is to develop a method by which a Deep Neural Network can be used to perform Fake News detection without implementing any complex Natural Language Processing techniques. A key element of the proposed structure consists of a data processing component that manipulates the news article data (shape, data type, etc.) in the database so that it can be used in-side a Deep Neural Network structure, without the need for additional complex NLP techniques. A Deep Neural Network model that can perform binary classification on a given set of news articles was built. The method was implemented in Python using the Jupyter Notebook framework. The algorithm has an accuracy of at least 70% on the testing data set, leading to conclusive results regarding the be-longing of the considered material to the fake news categories.
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References
Stephens, M.: A History of News, pp. 55–57, 3 edn. Oxford University Press, New York, USA (2007)
Fogg, B.J.: Stanford Guidelines for Web Credibility. A Research Summary from the Stanford Persuasive Technology Lab. Stanford University. www.webcredibility.org/guidelines. Accessed 10 Jan 2022
Bengio, Y.: Learning Deep Architectures for AI, pp. 13–18. Now Publishers, Norwell, Massachusetts, United States (2009)
Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, pp. 2–55, 3rd edn. Prentice Hall, Upper Saddle River. New Jersey (2003)
Intel: The Future of AI in Agriculture. https://www.intel.com/content/www/us/en/big-data/article/agriculture-harvests-big-data.html. Accessed 10 Jan 2022
The Guardian: The superhero of artificial intelligence: can this genius keep it in check? https://www.theguardian.com/technology/2016/feb/16/demis-hassabis-artificial-intelligencedeepmind-alphago. Accessed 10 Jan 2022
Ferrucci, D., Levas, A., Bagchi, S., Gondek, D., Mueller, E.T.: Watson: beyond Jeopardy! Artif. Intell. 199–200, 93–105 (2013)
Cortes, C., Vapnik, V.N.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Hosmer, D.W., Lemeshow, S.: Applied Logistic Regression, pp. 2–46, 2nd edn. Wiley Publishing (2000)
Borucka, A., Grzelak, M.: Application of logistic regression for production machinery efficiency evaluation. Appl. Sci. 9, 1–16 (2019)
Han, J., Morag, C.: The Influence of the Sigmoid Function Parameters on the Speed of Backpropagation Learning, pp. 195–201. Springer. Malaga-Torremolinos, Spain (1995)
Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)
Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks, Proceedings of the 14th International Conference on Artificial Intelligence and Statistics AISTATS, vol. 15, pp. 315–321. Montreal, Canada (2011)
Goodfellow I., Bengio Y., Courville A.: Back-Propagation and Other Differentiation Algorithms, pp. 200–220. MIT Press (2016)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning, pp. 326–339. MIT Press (2016)
Yi Tao, Z., Chellappa, R.: Computation of optical flow using a neural network. In: IEEE International Conference on Neural Networks, vol. 2, pp. 71–78 (1998)
Lawrence S., Giles, C.L., Ah Chung, T., Back, A.D.: Face recognition: a convolutional neural network approach. IEEE Trans. Neural Networks 8, 98–113 (1997)
Ciresan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3642–3649. Institute of Electrical and Electronics Engineers (IEEE), New York, NY (2012)
Klein, D., Manning, C.D.: Natural Language Grammar Induction Using a Constituent-Context Model. Advances in Neural Information Processing Systems. Standford University California, USA (2002)
Alrehamy, H.H., Walker, C.: SemCluster: unsupervised automatic Keyphrase extraction using affinity propagation. In: Chao, F., Schockaert, S., Zhang, Q. (eds.) UKCI 2017. AISC, vol. 650, pp. 222–235. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-66939-7_19
Ho Chiung, C., Baharim, K.N., Abdulsalam, A., Alias, M.S.: Deep neural networks for text: a review. In: Proceedings of the Sixth International Conference on Computer Science and Computational Mathematics (2017)
Luong, T., Socher, R., Manning, C.D.: Better Word Representations with Recursive Neural Networks for Morphology”. CoNLL, pp. 104–113 (2013)
Vlachos, A., Riedel, S.: Fact checking: Task definition and dataset construction. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social ScienceLondon, pp. 18–22. United Kingdom (2014)
Amazeen, M.A.: Checking the fact-checkers in 2008: predicting political ad scrutiny and assessing consistency. J. Political Market. 15(4), 433–464 (2008)
Morgan, M., Barker, D.C., Bowser, T.: Fact-checking polarized politics: does the fact-check industry provide consistent guidance on disputed realities? Forum 13(4) (2015)
Conroy, N., Rubin, V., Chen, Y.: Automatic deception detection: methods for finding fake news. In: Proceedings of the Association for Information Science and Technology. vol. 52, issue 1 (2015)
Thota, A., Tilak, P., Ahluwalia, S., Lohia, N.: Fake news detection: a deep learning approach. SMU Data Sci. Rev 1(3) (2018)
Baker, D., Chaudhry, A.K., Thun-Hohenstein, P.: Stance detection for the fake news challenge: identifying textual relationships with deep neural nets, Stanford. https://web.stanford.edu/class/archive/cs/cs224n/cs224n.1174/reports/2760230.pdf. Accessed 11 Jan 2022
Goodfellow, I.J., et al.: Generative Adversarial Nets, Machine Learning. Universite de Montreal, Montreal (2014)
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Avram, C., Mesaroş, G., Aştilean, A. (2022). Deep Neural Networks in Fake News Detection. In: Machado, J., et al. Innovations in Mechatronics Engineering II. icieng 2022. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-09385-2_3
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