A Smart Model to Detect Hindi Fake News for Social Media Platform using Hybrid Deep Learning
Keywords:
Hybrid Deep Learning, Hindi Fake News, Social Media PlatformAbstract
As a direct result, erroneous information spreads on those platforms very easily and quickly, leading to improper behaviours and outcomes. Sometimes individuals deliberately spread this erroneous information to upset others in order to advance their own objectives. Due to the rapid expansion of digital news, false information has already created severe hazards to the public's true judgment and trustworthiness. With the growing usage of social networking sites, which provide a fertile environment for its development and transmission, fake news has already put the public at tremendous risk. They made an effort to discover which algorithm was the most effective by contrasting the models they used. According to the results, the naive bayes algorithm may achieve accuracy of 83%, which is maximum. False news worsens the problem it already faces since it harms society as a whole in addition to the negative consequences it has on individuals. Due to the widespread dissemination of erroneous information, the "balance of the news ecosystem" may be upset.
Downloads
References
G. E. Dahl, T. N. Sainath and G. E. Hinton, Improving deep neural networks for LVCSR using rectified linear units and dropout, in: Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, pp. 8609–8613, 2013.
S. B. Davis and P. Mermelstein, Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences, in: Readings in Speech Recognition, ed: Elsevier, pp. 65–74, 1990.
J. Dean, G. S. Corrado, R. Monga, K. Chen, M. Devin, Q. V. Le, M. Mao, M. Ranzato, A. Senior, P. Tucker, K. Yang, A.Y. Ng, Large scale distributed deep networks, in: Advances in Neural Information Processing Systems, pp. 1223–1231, 2012.
L. Deng, O. Abdel-Hamid and D. Yu, A deep convolutional neural network using heterogeneous pooling for trading acoustic invariance with phonetic confusion, in: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6669–6673, 2013.
M. J. Gales, Maximum likelihood linear transformations for HMM-based speech recognition, Comput. Speech Lang. 12 (1998), 75–98.
X. Glorot and Y. Bengio, Understanding the diflculty of training deep feedforward neural networks, in: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249–256, 2010.
J. Goodfellow, D. Warde-Farley, M. Mirza, A. Courville and Y. Bengio, Maxout networks, in: Proceedings of the 30th International Conference on Machine Learning, Proceedings of Machine Learning Research, pp. 1319–1327, 2013.
Graves and J. Schmidhuber, Framewise phoneme classification with bidirectional LSTM and other neural network architectures, Neural Networks 18 (2005), 602–610.
Graves, N. Jaitly and A.-R. Mohamed, Hybrid speech recognition with deep bidirectional LSTM, in: Automatic Speech Recognition and Understanding (ASRU), 2013 IEEE Workshop on, pp. 273–278, 2013.
Graves, A.-R. Mohamed and G. Hinton, Speech recognition with deep recurrent neural networks, in: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649, 2013.
K. He, X. Zhang, S. Ren and J. Sun, Delving deep into rectifiers: surpassing human-level performance on imagenet classification, in: Proceedings of the IEEE international conference on computer vision, pp. 1026–1034, 2015.
G. Heigold, E. McDermott, V. Vanhoucke, A. Senior and M. Bacchiani, Asynchronous stochastic optimization for sequence training of deep neural networks, in: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5587–5591, 2014.
G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath and B. Kingsbury, Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups, IEEE Signal Proc. Mag. 29 (2012), 82–97.
K. Jarrett, K. Kavukcuoglu, M. A. Ranzato and Y. LeCun, What is the best multi-stage architecture for object recognition? in: 2009 IEEE 12th International Conference on Computer Vision, pp. 2146–2153, 2009.
Kingsbury, Lattice-based optimization of sequence classification criteria for neural-network acoustic modeling, in: Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on, pp. 3761–3764, Taipei, Taiwan, 2009.
Kingsbury, T. N. Sainath and H. Soltau, Scalable minimum Bayes risk training of deep neural network acoustic models using distributed Hessian-free optimization, Interspeech, pp. 10–13, Portland, OR, USA, 2012.
Krizhevsky, I. Sutskever and G. E. Hinton, Imagenet classification with deep convolutional neural networks, in: Advances in Neural Information Processing Systems, pp. 1097–1105, 2012.
N. Lambooij, Applying image recognition to automatic speech recognition: determining suitability of spectrograms for training a deep neural network for speech recognition, Bachelor thesis, Utrecht University, 2017.
Y. Lecun, L. Bottou, Y. Bengio and P. Haffner, Gradient-based learning applied to document recognition, in: Proceedings of the IEEE, vol. 86, pp. 2278–2324, November 1998.
L. Lee and R. C. Rose, Speaker normalization using eflcient frequency warping procedures, in: Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE International Conference on, pp. 353–356, Atlanta, GA, USA, 1996.
A.-R. Mohamed, G. Hinton and G. Penn, Understanding how deep belief networks perform acoustic modelling, in: Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on, pp. 4273–4276, Olomouc, Czech Republic, 2012.
T. Robinson, M. Hochberg and S. Renals, The use of recurrent neural networks in continuous speech recognition, in: C. H. Lee, F. K. Soong, and K. K. Paliwal, (Eds.), Automatic Speech and Speaker Recognition. The Kluwer International Series in Engineering and Computer Science (VLSI, Computer Architecture and Digital Signal Processing), vol. 355, pp. 233–258, Springer, Boston, MA, 1996.
T. N. Sainath, B. Kingsbury, A.-R. Mohamed, G. E. Dahl, G. Saon, H. Soltau, T. Beran, A. Y. Aravkin and B. Ramabhadran, Improvements to deep convolutional neural networks for LVCSR, in: 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 315–320, Olomouc, Czech Republic, 2013.
T. N. Sainath, A.-R. Mohamed, B. Kingsbury and B. Ramabhadran, Deep convolutional neural networks for LVCSR, in: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 8614–8618, Vancouver, Canada, May 26–31, 2013.
Ranjan et al. “Part of speech tagging and local word grouping techniques for natural language parsing in Hindi”. In Proceedings of the 1st International Conference on Natural Language Processing (ICON 2003). Semanticscholar. 2003
MonaDiab et al. Automatic Tagging of Arabic Text: From Raw Text to Base Phrase Chunks. Proceedings of HLT-NAACL 2004: Short Papers (pp. 149–152). Boston, Massachusetts, USA: Association for Computational Linguistics. 2004
Rouse, M. https://searchenterpriseai.techtarget.com/definition/machine-learning-ML May 2018.
Sumeet Dua, Xian Du. “Data Mining and Machine Learning in Cybersecurity”. New York: Auerbach Publications.19 April 2016.
RAY, S. https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/ 2017, September
Huang, T.-Q. (n.d.) https://www.researchgate.net/figure/Pseudo-code-of-information-gain-basedrecursive-feature-elimination-procedure-with-SVM_fig2_228366941 2018
Researchgate.net. Available at: Available at: https://www.researchgate.net/figure/Pseudocode-ofnaive-bayes-algorithm_fig2_325937073. 2018
E. Tacchini, G. Ballarin, M. L. Della Vedova, S. Moret and L. de Alfaro, “Some like it hoax: Automated fake news detection in social networks,” 2017.
Kumar, S. Singh, G. Kaur, Fake news detection of indian and united states election data using machine learning algorithm (2019)
F. Rangel, A. Giachanou, B. Ghanem, P. Rosso, Overview of the 8th author profiling task at PAN 2020: profiling fake news spreaders on twitter, volume 2696, CEUR-WS.org, 2020
Vogel, P. Jiang, Fake news detection with the new German dataset “GermanFakeNC”, in: International Conference on Theory and Practice of Digital Libraries, Springer, 2019, pp. 288–295.
Suarez, P.O.; Sagot, B.; Romary, L. Asynchronous Pipeline for Processing Huge Corpora on Medium to Low Resource Infrastructures. In Proceedings of the 7thWorkshop on the Challenges in the Management of Large Corpora (CMLC-7), Cardiff, UK, 22 July 2019; Leibniz-Institut für Deutsche Sprache: Mannheim. Germany, 2019; pp. 9–16.
Derczynski, L., Bontcheva, K., Liakata, M., Procter, R., Hoi, G. W. S., & Zubiaga, A. (2017). Semeval-2017 task 8: Rumoureval: Determining rumour veracity and support for rumours. arXiv:1704.05972.
Derczynski, L., Bontcheva, K., Lukasik, M., Declerck, T., Scharl, A., Georgiev, G., Stewart, R., et al. (2015). Pheme: Computing veracity-the fourth challenge of big social data. In Proceedings of the extended semantic web conference eu project networking session (ESCW-PN) (p. n/a).
Dhillon, A., & Verma, G. K. (2020). Convolutional neural network: A review of models, methodologies and applications to object detection. Progress in Artificial Intelligence, 9(2), 85–112.
Drumond, T. F., Viéville, T., & Alexandre, F. (2019). Bio-inspired analysis of deep learning on not-so-big data using data-prototypes. Frontiers in Computational Neuroscience, 12, 100.
Elhadad, M. K., Li, K. F., & Gebali, F. (2019). A novel approach for selecting hybrid features from online news textual metadata for fake news detection. In International conference on p2p, parallel, grid, cloud and internet computing (pp. 914–925). Springer.
Ferreira, W., & Vlachos, A. (2016). Emergent: a novel data-set for stance classification. In Proceedings of the 2016 conference of the north american chapter of the association for computational linguistics: Human language technologies (pp. 1163–1168).
Gorrell, G., Kochkina, E., Liakata, M., Aker, A., Zubiaga, A., Bontcheva, K., & Derczynski, L. (2019). Semeval-2019 task 7: Rumoureval, determining rumour veracity and support for rumours. In Proceedings of the 13th international workshop on semantic evaluation (pp. 845–854).
Granger, E., Kiran, M., Blais-Morin, L.-A., et al. (2017). A comparison of CNN-based face and head detectors for real-time video surveillance applications. In 2017 seventh international conference on image processing theory, tools and applications (IPTA) (pp. 1–7). IEEE.
Graves, A. (2012). Long short-term memory. In Supervised sequence labelling with recurrent neural networks (pp. 37–45). Springer.
Hamdi, T., Slimi, H., Bounhas, I., & Slimani, Y. (2020). A hybrid approach for fake news detection in twitter based on user features and graph embedding. In International conference on distributed computing and internet technology (pp. 266–280). Springer. J
in, Z., Cao, J., Zhang, Y., & Luo, J. (2016). News verification by exploiting conflicting social viewpoints in microblogs. In Thirtieth AAAI conference on artificial intelligence (pp. 2972–2978).
Kar, A. K. (2016). Bio inspired computing–a review of algorithms and scope of applications. Expert Systems with Applications, 59, 20–32.
Agarwalla, K., Nandan, S., Nair, V. A., & Hema, D. D. (2019). Fake news detection using machine learning and Natural Language Processing. International Journal of Recent Technology and Engineering (IJRTE), 7(6), 844-847.
Guo, Z., Shen, Y., Bashir, A. K., Imran, M., Kumar, N., Zhang, D., & Yu, K. (2020). Robust spammer detection using collaborative neural network in Internet-of-Things applications. IEEE Internet of Things Journal, 8(12), 9549-9558.
Kumar, S.A.S., Naveen, R., Dhabliya, D., Shankar, B.M., Rajesh, B.N. Electronic currency note sterilizer machine (2020) Materials Today: Proceedings, 37 (Part 2), pp. 1442-1444.
Sherje, N.P., Agrawal, S.A., Umbarkar, A.M., Kharche, P.P., Dhabliya, D. Machinability study and optimization of CNC drilling process parameters for HSLA steel with coated and uncoated drill bit (2021) Materials Today: Proceedings, .
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
