A Survey on Fake News Detection using Support Vector Model

Authors

  • Himani Kanojia  Computer Engineering Department, Government Engineering College Modasa, Gujarat, India
  • Prof. Rahul Vaza  Computer Engineering Department, Government Engineering College Modasa, Gujarat, India

DOI:

https://doi.org/10.32628/IJSRSET229225

Keywords:

Artificial Intelligence, Machine Learning, SVM, Text Classification, Sentiment Analysis

Abstract

In the boosting period of Social Media availability and easy availability of Internet to end users in various regions, many challenges are also occurred with usage of this technology. Fake news spreading in various field is also a major challenge in recent time. Fake news has been spreading into vast in significant numbers for various business reasons and also for political reasons. Problem of fake news has become frequent in the online world. People can get affected and their view are also affected easily by these type of fake news for its fabricated words. This type of news has enormous effects on the offline community in various sectors. In this way it is an interesting topic for research. Significant research has been conducted on the detection of fake news from English texts and other languages but there is chancel to improve the work with other languages as well as from multiple sources. Various algorithms like SVM and other supervised algorithm can be helpful to classify fake news. As Sentiment Score is an also a major point for detection of Fake news; in our work we are applying SVM algorithm with TF/IDF, multiple Languages (Language Conversation) etc.”

References

  1. Hussain, Md Gulzar, et al. "Detection of bangla fake news using mnb and svm classifier." 2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE). IEEE, 2020.
  2. M. Umer, Z. Imtiaz, S. Ullah, A. Mehmood, G. S. Choi and B. -W. On, "Fake News Stance Detection Using Deep Learning Architecture (CNN-LSTM)," in IEEE Access, vol. 8, pp. 156695-156706, 2020, doi: 10.1109/ACCESS.2020.3019735.
  3. R. K. Kaliyar, P. Kumar, M. Kumar, M. Narkhede, S. Namboodiri and S. Mishra, "DeepNet: An Efficient Neural Network for Fake News Detection using News- User Engagements," 2020 5th International Conference on Computing, Communication and Security (ICCCS), 2020, pp. 1-6, doi: 10.1109/ICCCS49678.2020.9277353.
  4. Y. Ahn and C. Jeong, "Natural Language Contents Evaluation System for Detecting Fake News using Deep Learning," 2019 16th International Joint Conference on Computer Science and Software Engineering (JCSSE), 2019, pp. 289-292, doi: 10.1109/JCSSE.2019.8864171.
  5. A. Kesarwani, S. S. Chauhan and A. R. Nair, "Fake News Detection on Social Media using K-Nearest Neighbor Classifier," 2020 International Conference on Advances in Computing and Communication Engineering (ICACCE), 2020, pp. 1-4, doi: 10.1109/ICACCE49060.2020.9154997.
  6. B. Bhutani, N. Rastogi, P. Sehgal and A. Purwar, "Fake News Detection Using Sentiment Analysis," 2019 Twelfth International Conference on Contemporary Computing (IC3), 2019, pp. 1-5, doi: 10.1109/IC3.2019.8844880.
  7. I. Vogel and M. Meghana, "Detecting Fake News Spreaders on Twitter from a Multilingual Perspective," 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), 2020, pp. 599- 606, doi: 10.1109/DSAA49011.2020.00084.
  8. Bani-Hani, Anoud I. et al. “A Semantic Model for Context-Based Fake News Detection on Social Media.” 2020 IEEE/ACS 17th International Conference on Computer Systems and Applications (AICCSA) (2020): 1-7.
  9. K. Xu, F. Wang, H. Wang and B. Yang, "Detecting fake news over online social media via domain reputations and content understanding," in Tsinghua Science and Technology, vol. 25, no. 1, pp. 20-27, Feb. 2020, doi: 10.26599/TST.2018.9010139.
  10. A. Benamira, B. Devillers, E. Lesot, A. K. Ray, M. Saadi and F. D. Malliaros, "Semi-Supervised Learning and Graph Neural Networks for Fake News Detection," 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2019, pp. 568-569, doi: 10.1145/3341161.3342958.
  11. https://towardsdatascience.com/using-a-i-to-combat- fake-news-34f5a51907d6
  12. Dataset, https://www.kaggle.com/c/fake-news/data
  13. https://www.researchgate.net/profile/Ziyad_Saleh2/p ublication/332548325_Artificial_ Intelligence_ Definition_Ethics_and_Standards/links/5cbc3cc492851 c8d22fad1a6/Artificial-Intelligence-Definition-Ethics- and-Standards
  14. Kaliyar, Rohit & Goswami, Anurag & Narang, Pratik. (2021). DeepFakE: improving fake news detection using tensor decomposition-based deep neural network. The Journal of Supercomputing. 77. 10.1007/s11227-020-03294-y.
  15. https://miro.medium.com/max/602/0*qf-O7Jm1mmZrXYqA
  16. Zhao, Liang & Hua, Ting & Lu, Chang-Tien & Chen, Ing- Ray. (2015). A Topic-focused Trust Model for Twitter. Computer Communications. 14. 38-1. 10.1016/j.comcom.2015.08.001.

International Journal of Scientific Research in Science, Engineering and Technology

Downloads

Published

2022-04-30

Issue

Volume 9, Issue 2, March-April-2022

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

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

How to Cite

[1]
Himani Kanojia, Prof. Rahul Vaza "A Survey on Fake News Detection using Support Vector Model" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 9, Issue 2, pp.125-131, March-April-2022. Available at doi : https://doi.org/10.32628/IJSRSET229225