Using Emerging Deep Convolutional Neural Networks (DCNN) Learning Techniques for Detecting Phony News

Authors

  • Kajal  Assistant Professor, Department of Computer Science & Engineering, M.G Institute of Management & Technology, Lucknow, U.P
  • Neha Singh  Assistant Professor, Department of Computer Science & Engineering, RR Institute of Modern Technology, Lucknow, U.P, India
  • Dr. Nikhat Akhtar  Associate Professor, Department of Information Technology, Goel Institute of Technology & Management, Lucknow, U.P, India
  • Ms. Sana Rabbani  Assistant Professor, Department of Information Technology, Goel Institute of Technology & Management, Lucknow, U.P, India
  • Dr. Yusuf Perwej  Professor, Department of Computer Science & Engineering, Goel Institute of Technology & Management, Lucknow, U.P, India
  • Susheel Kumar  Assistant Professor, Department of Computer Science & Engineering, Ambalika Institute of Management & Technology, Lucknow, India

DOI:

https://doi.org/10.32628/CSEIT2410113

Keywords:

Phony Profile, Deep Learning, Detection, Phony News, Classification, 4TU.ResearchData Set, Machine Learning, Deep Convolutional Neural Networks (DCNN).

Abstract

Over the years, social media has revolutionized the way people share and receive information. The rapid dissemination of false information is another concern that may have negative consequences for individuals and society as a whole. For several economic and political reasons, fake news has started appearing online often and in massive amounts. One of the many stylistic tactics used by fake news producers to make their articles more appealing is appealing to readers' emotions. One of the many stylistic tactics used by fake news producers to make their articles more appealing is to appeal to readers' emotions. This has made it very challenging to identify fake news stories and help their producers validate them via data processing channels without deceiving the audience. Claims, particularly those that gain thousands of views and likes before being challenged and debunked by credible sources, need a method for fact-checking. In order to properly detect and classify fake news, many machine learning techniques have been implemented. In this experiment, an ML classifier was employed to ascertain the veracity of news reports. The best features of the dataset are used to evaluate the proposed model in comparison to other benchmark approaches. Our proposed model (DCNNs) outperforms the state-of-the-art methods in terms of classification accuracy (99.23 percent).

References

  1. Ms Farah Shan, Versha Verma, Apoorva Dwivedi, Y. Perwej, Ashish Kumar Srivastava, “Novel Approaches to Detect Phony Profile on Online Social Networks (OSNs) Using Machine Learning”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 9, Issue 3, Pages 555-568, 2023, DOI: 10.32628/CSEIT23903126
  2. de Oliveira, N.R.; Medeiros, D.S.V.; Mattos, D.M.F. A Sensitive Stylistic Approach to Identify Fake News on Social Networking. IEEE Signal Process. Lett., 27, 1250–1254, 2020
  3. S. Shankar, D. Robertson, Y. Ioannou, A. Criminisi and R. Cipolla, "Refining architectures of deep convolutional neural networks", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2212-2220, 2016
  4. Y. W. Q. H. Jiaxiang Wu, Cong Leng and J. Cheng, "Quantized convolutional neural networks for mobile devices", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016
  5. Sachin Bhardwaj, Apoorva Dwivedi, Ashutosh Pandey, Y. Perwej, Pervez Rauf Khan, “Machine Learning-Based Crowd Behavior Analysis and Forecasting”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 9, Issue 3, Pages 418-429, May-June 2023-2023, DOI: 10.32628/CSEIT23903104
  6. Goldani, M.H.; Momtazi, S.; Safabakhsh, R. Detecting fake news with capsule neural networks. Appl. Soft Comput. 101, 106991, 2021
  7. Buhler, J.; Murawski, M.; Darvish, M.; Bick, M. Developing a Model to Measure Fake News Detection Literacy of Social Media Users. In Disinformation, Misinformation, and Fake News in Social Media; Springer: Basel, Switzerland, pp. 213–227, 2020
  8. Shu, K.; Sliva, A.; Wang, S.; Tang, J.; Liu, H. Fake news detection on social media: A data mining perspective. ACM SIGKDD Explor. Newslett., 19, 22–36, 2017
  9. Kaliyar, R.K.; Goswami, A.; Narang, P. EchoFakeD: Improving fake news detection in social media with an efficient deep neural network. Neural Comput. Appl., 33, 8597–8613, 2021
  10. Y. Perwej, Firoj Parwej, “A Neuroplasticity (Brain Plasticity) Approach to Use in Artificial Neural Network”, International Journal of Scientific & Engineering Research (IJSER), France , ISSN 2229 – 5518, Volume 3, Issue 6, Pages 1- 9, 2012, DOI: 10.13140/2.1.1693.2808
  11. Potthast, M.; Kiesel, J.; Reinartz, K.; Bevendorff, J.; Stein, B. A stylometric inquiry into hyperpartisan and fake news. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics; ACL: Melbourne, Australia, Volume 1, pp. 231–240, July 2018
  12. Jiang T, Li JP, Haq AU, Saboor A, Ali A,”A novel stacking approach for accurate detection of fake news”, IEEE Access 9:22626–22639, 2021
  13. Yusuf Perwej, “An Optimal Approach to Edge Detection Using Fuzzy Rule and Sobel Method”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (IJAREEIE), ISSN (Print) : 2320 – 3765, ISSN (Online): 2278 – 8875, Volume 4, Issue 11, Pages 9161-9179, 2015, DOI: 10.15662/IJAREEIE.2015.0411054
  14. Chen W, Zhang Y, Yeo CK, Lau CT, Sung Lee B,”Unsupervised rumor detection based on users’ behaviors using neural networks”, Pattern Recogn Lett 105:226–233, 2018
  15. Y. Perwej, “Recurrent Neural Network Method in Arabic Words Recognition System”, International Journal of Computer Science and Telecommunications (IJCST), Sysbase Solution (Ltd), UK, London, (http://www.ijcst.org) , ISSN 2047-3338, Volume 3, Issue 11, Pages 43-48, 2012
  16. Farghaly, A.; Shaalan, K.: Arabic natural language processing: challenges and solutions. ACM Trans. Asian Lang. Inf. Process. 8(4), 1–22, 2009
  17. Bhavesh Kumar Jaisawal, Dr. Yusuf Perwej, Sanjay Kumar Singh, Susheel Kumar, Jai Pratap Dixit, Niraj Kumar Singh, “An Empirical Investigation of Human Identity Verification Methods” , International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Volume 10, Issue 1, Pages 16-38, 2022, DOI: 10.32628/IJSRSET2310012
  18. Allcott H, Gentzkow M,” Social media and fake news in the 2016 election”, J Econ Perspect 31(2):211–36, 2017
  19. Jin Z, Cao J, Zhang Y, Zhou J, Tian Q.” Novel visual and statistical image features for microblogs news verification”, IEEE Trans Multimed 19(3):598–608, 2016
  20. Y. Perwej, Firoj Parwej, Asif Perwej, “Copyright Protection of Digital Images Using Robust Watermarking Based on Joint DLT and DWT”, International Journal of Scientific & Engineering Research (IJSER), France, ISSN 2229-5518, Volume 3, Issue 6, Pages 1- 9, 2012
  21. Y. Perwej, Asif Perwej, Firoj Parwej, “An Adaptive Watermarking Technique for the copyright of digital images and Digital Image Protection”, International journal of Multimedia & Its Applications (IJMA), Academy & Industry Research Collaboration Center (AIRCC), USA, Volume 4, No.2, Pages 21- 38, 2012, DOI: 10.5121/ijma.2012.4202
  22. Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu. 2017. Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsletter 19, 1 (2017), 22–36
  23. Elhadad MK, Li KF, Gebali F,” Detecting misleading information on COVID-19”, IEEE Access 8:165201–165215, 2020
  24. Potthast, Martin and Kopsel, Sebastian and Stein, Benno and Hagen, Matthias., “Clickbait detection.” ¨ European Conference on Information Retrieval 810–817, 2016
  25. Nikhat Akhtar, Devendera Agarwal, “An Efficient Mining for Recommendation System for Academics”, International Journal of Recent Technology and Engineering (IJRTE), ISSN 2277-3878 (online), SCOPUS, Volume-8, Issue-5, Pages 1619-1626, 2020 , DOI: 10.35940/ijrte.E5924.018520
  26. Y. Perwej, “Unsupervised Feature Learning for Text Pattern Analysis with Emotional Data Collection: A Novel System for Big Data Analytics”, IEEE International Conference on Advanced computing Technologies & Applications (ICACTA'22), SCOPUS, IEEE No: #54488 ISBN No Xplore: 978-1-6654-9515-8, Coimbatore, India, 4-5 March 2022, DOI: 10.1109/ICACTA54488.2022.9753501
  27. Zhou, X., Zafarani, R.: A survey of fake news: fundamental theories, detection methods, and opportunities. ACM Comput. Surv. (CSUR) 53(5), 1–40. 2020
  28. Neves JC et al,”GANprintR: improved fakes and evaluation of the state of the art in face manipulation detection”, IEEE J Sel Top Signal Proc 14(5):1038–1048, 2020
  29. Arkaitz Zubiaga, Ahmet Aker, Kalina Bontcheva, Maria Liakata, and Rob Procter. 2018. Detection and resolution of rumours in social media: A survey. ACM Computing Surveys (CSUR) 51, 2, 32, 2018
  30. Yusuf Perwej, Shaikh Abdul Hannan, Nikhat Akhtar, “The State-of-the-Art Handwritten Recognition of Arabic Script Using Simplified Fuzzy ARTMAP and Hidden Markov Models”, International Journal of Computer Science and Telecommunications (IJCST), Sysbase Solution (Ltd), UK, London, Volume, Issue 8, Pages 26 - 32, 2014
  31. Zhou X, Zafarani R ,“Fake news: a survey of research, detection methods, and opportunities”, 2018, arXiv:arXiv-1812 54
  32. Wang WY Liar, liar pants on fire: A new benchmark dataset for fake news detection. In: Proceedings of the 55th annual meeting of the association for computational linguistics (vol 2: short Papers), pp 422–426, 2017
  33. Yusuf Perwej, “The Bidirectional Long-Short-Term Memory Neural Network based Word Retrieval for Arabic Documents”, Transactions on Machine Learning and Artificial Intelligence (TMLAI), Society for Science and Education, United Kingdom (UK), ISSN 2054-7390, Volume 3, Issue 1, Pages 16 - 27, 2015, DOI: 10.14738/tmlai.31.863
  34. Baptista JP, Gradim A,”Understanding fake news consumption: a review”, Soc Sci 9(10):185, 2020
  35. Firoj Parwej, Nikhat Akhtar, Dr. Yusuf Perwej, “An Empirical Analysis of Web of Things (WoT)”, International Journal of Advanced Research in Computer Science (IJARCS), ISSN: 0976-5697, Volume 10, No. 3, Pages 32-40, May 2019, DOI: 10.26483/ijarcs.v10i3.6434
  36. Shu K, Wang S, Liu H,”Exploiting tri-relationship for fake news detection”, Association for the Advancement of Artifcial Intelligence, arXiv preprint arXiv:1712.07709, 2017
  37. Jin Z, Cao J, Zhang Y, Zhou J, Tian Q,” Novel visual and statistical image features for microblogs news verification”, IEEE Trans Multimed 19(3):598–608, 2016
  38. Saurabh Sahu, Km Divya, Neeta Rastogi, Puneet Kumar Yadav, Y.Perwej, “Sentimental Analysis on Web Scraping Using Machine Learning Method” , Journal of Information and Computational Science (JOICS), ISSN: 1548-7741, Volume 12, Issue 8, Pages 24-29, August 2022, DOI: 10.12733/JICS.2022/V12I08.535569.67004
  39. Zhou X, Zafarani R, Shu K, Liu H (2019) Fake news: fundamental theories, detection strategies and challenges, In: Proceedings of the twelfth ACM international conference on web search and data mining, WSDM’19. Association for Computing Machinery, New York, NY, USA, pp 836–837
  40. Dawar Husain, Dr. Yusuf Perwej, Satendra Kumar Vishwakarma, Prof. (Dr.) Shishir Rastogi, Vaishali Singh, Nikhat Akhtar, “Implementation and Statistical Analysis of De-noising Techniques for Standard Image”, International Journal of Multidisciplinary Education Research (IJMER), ISSN:2277-7881, Volume 11, Issue10 (4), Pages 69-78, 2022, DOI: 10.IJMER/2022/11.10.72
  41. Qi P, Cao J, Yang T, Guo J, Li J, “Exploiting multi-domain visual information for fake news detection”, In: 2019 IEEE international conference on data mining (ICDM), pp 518–527, 2019
  42. Golbeck, J.; Mauriello, M.; Auxier, B.; Bhanushali, K.H.; Bonk, C.; Bouzaghrane, M.A.; Buntain, C.; Chanduka, R.; Cheakalos, P.; Everett, J.B.; et al. Fake News vs Satire: A Dataset and Analysis; WebSci ’18; Association for Computing Machinery: New York, NY, USA, pp. 17–21, 2018
  43. Y. Perwej, Dr. Shaikh Abdul Hannan, Firoj Parwej, Nikhat Akhtar, “A Posteriori Perusal of Mobile Computing”, International Journal of Computer Applications Technology and Research (IJCATR), ATS (Association of Technology and Science), India, ISSN 2319–8656 (Online), Volume 3, Issue 9, Pages 569 - 578, 2014, DOI: 10.7753/IJCATR0309.1008
  44. C.-Y. Lin, T.-Y. Li and P. Chen, "An Information Visualization System to Assist News Topics Exploration with Social Media", ACMDL, July 2016
  45. Mykhailo Granik and Volodymyr Mesyura, "Fake news detection using naive bayes classifier", 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), pp. 900-903, 2017
  46. Y. Perwej, Kashiful Haq, Uruj Jaleel, Firoj Perwej, “Block Ciphering in KSA, A Major Breakthrough in Cryptography Analysis in Wireless Networks”, International Transactions in Mathematical Sciences and Computer, India, ISSN-0974-5068, Volume 2, No. 2, Pages 369-385, 2009
  47. Niall J Conroy, Victoria L Rubin and Yimin Chen, "Automatic deception detection: Methods for finding fake news", Proceedings of the Association for Information Science and Technology, vol. 52, no. 1, pp. 1-4, 2015
  48. Allcott, H. and Gentzkow, M.,” Social media and fake news in the 2016 election”, Journal of Economic Perspectives, 31(2):211–36, 2017
  49. Shobhit Kumar Ravi, Shivam Chaturvedi, Dr. Neeta Rastogi, Dr. Nikhat Akhtar, Dr. Yusuf Perwej, “A Framework for Voting Behavior Prediction Using Spatial Data”, International Journal of Innovative Research in Computer Science & Technology (IJIRCST), Volume 10, Issue 2, Pages 19-28, 2022, DOI: 10.55524/ijircst.2022.10.2.4
  50. Du, J., Dou, Y., Xia, C., Cui, L., Ma, J., Yu, P.S.: Cross-lingual COVID-19 fake news detection. In: IEEE International Conference on Data Mining Workshops ICDMW, vol. 2021-December, pp. 859–862 (2021)
  51. Rubin, V.L., Conroy, N.J., Chen, Y., Cornwell, S.: Fake News or Truth? Using Satirical Cues to Detect Potentially Misleading News, pp. 7–17. https://youtu.be/2X93u3anTco. Accessed 29 July 2022
  52. A. Al-Sideiri, Z. B. C. Cob, and S. B. M. Drus, Machine Learning Algorithms for Diabetes Prediction: A Review Paper,‖ ACM Int. Conf. Proceeding Ser., pp. 27–32, 2019, doi: 10.1145/3388218.3388231.
  53. Y. Perwej, Dr. Ashish Chaturvedi, “Machine Recognition of Hand Written Characters using Neural Networks”, International Journal of Computer Applications (IJCA), USA, ISSN 0975 – 8887, Volume 14, No. 2, Pages 6- 9, 2011, DOI: 10.5120/1819-2380
  54. Dr. E. Baraneetharan, ,”Role of Machine Learning Algorithms Intrusion Detection in WSNs: A Survey”, ‖ J. Inf. Technol. Digit. World, vol. 02, no. 03, pp. 161– 173, 2020, doi: 10.36548/jitdw.2020.3.004.
  55. Y. Perwej, Firoj Parwej, Nikhat Akhtar, “An Intelligent Cardiac Ailment Prediction Using Efficient ROCK Algorithm and K- Means & C4.5 Algorithm”, European Journal of Engineering Research and Science (EJERS), Bruxelles, Belgium, ISSN: 2506-8016 (Online), Vol. 3, No. 12, Pages 126 – 134, 2018, DOI: 10.24018/ejers.2018.3.12.989
  56. Y. Perwej, Firoj Parwej, “A Neuroplasticity (Brain Plasticity) Approach to Use in Artificial Neural Network”, International Journal of Scientific & Engineering Research (IJSER), France , ISSN 2229 – 5518, Volume 3, Issue 6, Pages 1- 9, 2012, DOI: 10.13140/2.1.1693.2808
  57. K. Thai, T. M. Tu, T. Q. Bui and T.-T. Bui, "Gradient tree boosting machine learning on predicting the failure modes of the RC panels under impact loads", Eng. Comput, pp. 1-12, 2019
  58. C. Zhenhai and Liu. Wei, "Logistic Regression Model and Its Application", Journal of Yanbian University (Natural Science Edition), vol. 38, no. 01, pp. 28-32, 2012
  59. A. Telikani, A. Tahmassebi, W. Banzhaf, and A. H. Gandomi, Evolutionary Machine Learning: A Survey,‖ ACM Comput. Surv., vol. 54, no. 8, 2022
  60. Wei Xiong, Bo Du, Lefei Zhang, Ruimin Hu and Dacheng Tao, "Regularizing Deep Convolutional Neural Networks with a Structured Decorrelation Constraint", IEEE 16th International Conference on Data Mining (ICDM), pp. 3366-3370, 2016
  61. A. Perwej, K.P. Yadav, V. Sood and Y. Perwej, “An Evolutionary Approach to Bombay Stock Exchange Prediction with Deep Learning Technique”, IOSR Journal on Business Management, Vol. 20, No. 12, pp. 63-79, 2018
  62. J. Fan, W. Xu, Y. Wu and Y. Gong, "Human tracking using convolutional neural networks", Neural Networks IEEE Transactions, 2010
  63. Nikhat Akhtar, Devendera Agarwal, “An Efficient Mining for Recommendation System for Academics”, International Journal of Recent Technology and Engineering (IJRTE), ISSN 2277-3878 (online), SCOPUS, Volume-8, Issue-5, Pages 1619-1626, 2020, DOI: 10.35940/ijrte.E5924.018520
  64. Y. Freund and R. Schapire, "A Decision-Theoretic Generalization of Online Learning and an Application to Boosting", Journal of Computer and System Sciences, vol. 55, pp. 119-139, 1997
  65. Neha Kulshrestha, Nikhat Akhtar, Y. Perwej, “Deep Learning Models for Object Recognition and Quality Surveillance”, International Conference on Emerging Trends in IoT and Computing Technologies (ICEICT-2022), ISBN 978-10324-852-49, SCOPUS, Routledge, Taylor & Francis, CRC Press, Chapter 75, pages 508-518, Goel Institute of Technology & Management, 2022, DOI: 10.1201/9781003350057-75
  66. J. C. Reis, A. Correia, F. Murai, A. Veloso and F. Benevenuto, "Supervised learning for fake news detection", IEEE Intelligent Systems, vol. 34, no. 2, pp. 76-81, 2019
  67. S. Gilda, "Notice of violation of ieee publication principles: Evaluating machine learning algorithms for fake news detection", 2017 IEEE 15 th student conference on research and development (SCOReD). IEEE, pp. 110-115, 2017
  68. Y. Perwej, “An Optimal Approach to Edge Detection Using Fuzzy Rule and Sobel Method”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (IJAREEIE), ISSN (Print) : 2320 – 3765, ISSN (Online): 2278 – 8875, Volume 4, Issue 11, Pages 9161-9179, 2015, DOI: 10.15662/IJAREEIE.2015.0411054
  69. Y. Perwej, Dr. Ashish Chaturvedi, “Machine Recognition of Hand Written Characters using Neural Networks”, International Journal of Computer Applications (IJCA), USA, ISSN 0975 – 8887, Volume 14, No. 2, Pages 6- 9, 2011, DOI: 10.5120/1819-2380
  70. Prof. Kameswara Rao Poranki, Dr. Yusuf Perwej, Dr. Asif Perwej,” The Level of Customer Satisfaction related to GSM in India “, TIJ's Research Journal of Science & IT Management – RJSITM, International Journal's-Research Journal of Science & IT Management of Singapore, ISSN: 2251-1563, Singapore, in www.theinternationaljournal.org as RJSSM, Volume 04, Number: 03, Pages 29-36 , 2015
  71. G. Cheng, P. Zhou and J. Han, "Rifd-cnn: Rotation-invariant and fisher discriminative convolutional neural networks for object detection", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2884-2893, 2016
  72. L. Cui, K. Shu, S. Wang, D. Lee and H. Liu, "defend: A system for explainable fake news detection", Proceedings of the 28th ACM international conference on information and knowledge management, pp. 2961-2964, 2019
  73. Y. LeCun, Y. Bengio and G. E. Hinton, "Deep learning", Nature, vol. 521, pp. 436-444, May 2015
  74. E. Shelhamer, J. Long and T. Darrell, "Fully convolutional networks for semantic segmentation", IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 4, pp. 640-651, 2017

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2024-02-29

Issue

Volume 10, Issue 1, January-February-2024

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Kajal, Neha Singh, Dr. Nikhat Akhtar, Ms. Sana Rabbani, Dr. Yusuf Perwej, Susheel Kumar, " Using Emerging Deep Convolutional Neural Networks (DCNN) Learning Techniques for Detecting Phony News" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 10, Issue 1, pp.122-137, January-February-2024. Available at doi : https://doi.org/10.32628/CSEIT2410113