CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Detection of Fake News using Machine Learning Models

by Velivela Durga Lakshmi, Ch Sita Kumari
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 47
Year of Publication: 2022
Authors: Velivela Durga Lakshmi, Ch Sita Kumari
10.5120/ijca2022921874

Velivela Durga Lakshmi, Ch Sita Kumari . Detection of Fake News using Machine Learning Models. International Journal of Computer Applications. 183, 47 ( Jan 2022), 22-27. DOI=10.5120/ijca2022921874

@article{ 10.5120/ijca2022921874,
author = { Velivela Durga Lakshmi, Ch Sita Kumari },
title = { Detection of Fake News using Machine Learning Models },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2022 },
volume = { 183 },
number = { 47 },
month = { Jan },
year = { 2022 },
issn = { 0975-8887 },
pages = { 22-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number47/32247-2022921874/ },
doi = { 10.5120/ijca2022921874 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:20:14.320828+05:30
%A Velivela Durga Lakshmi
%A Ch Sita Kumari
%T Detection of Fake News using Machine Learning Models
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 47
%P 22-27
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the present-day scenario, it is becoming a big problem to find whether a piece of news is real or fake. It is causing great loss to the individual and organization. The news articles can be from news channels or any other sources. In this project, the fake news is detected based on text, title, and author as parameters and converting them into vectors using Term Frequency- Inverse Document Frequency (TF-IDF) and Count vectorizers. On the vectors, PCA was applied to reduce the dimensions. The reduced vectors were given as input to the supervised machine learning algorithms. The resultant performance of algorithms was analyzed based on accuracy, precision, and recall. Hence, Random Forest classifier along with Count vectorizer gives the best technique for detection of the authenticity of the news.

References
  1. K. Poddar, G. B. Amali D. and K. S. Umadevi, "Comparison of Various Machine Learning Models for Accurate Detection of Fake News," 2019 Innovations in Power and Advanced Computing Technologies (i-PACT), Vellore, India, 2019, pp. 1-5, doi: 10.1109/i-PACT44901.2019.8960044.
  2. Abdullah-All-Tanvir, E. M. Mahir, S. Akhter and M. R. Huq, "Detecting Fake News using Machine Learning and Deep Learning Algorithms," 2019 7th International Conference on Smart Computing & Communications (ICSCC), Sarawak, Malaysia, 2019, pp. 1-5, doi: 10.1109/ICSCC.2019.8843612.
  3. J. C. S. Reis, A. Correia, F. Murai, A. Veloso and F. Benevenuto, "Supervised Learning for Fake News Detection," in IEEE Intelligent Systems, vol. 34, no. 2, pp. 76-81, March-April 2019, doi: 10.1109/MIS.2019.2899143.
  4. N. R. de Oliveira, D. S. V. Medeiros and D. M. F. Mattos, "A Sensitive Stylistic Approach to Identify Fake News on Social Networking," in IEEE Signal Processing Letters, vol. 27, pp. 1250-1254, 2020, doi: 10.1109/LSP.2020.3008087.
  5. R. R. Mandical, N. Mamatha, N. Shivakumar, R. Monica and A. N. Krishna, "Identification of Fake News Using Machine Learning," 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Bangalore, India, 2020, pp. 1-6, doi: 10.1109/CONECCT50063.2020.9198610.
  6. Agarwal, A. and Dixit, A., 2020. Fake News Detection: An Ensemble Learning Approach. 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS),.
  7. F. Muhammad and S. Ahmed, "Fake Review Detection using Principal Component Analysis and Active Learning", International Journal of Computer Applications, vol. 178, no. 48, pp. 42-48, 2019.
  8. J. Shaikh and R. Patil, "Fake News Detection using Machine Learning," 2020 IEEE International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC), 2020, pp. 1-5, doi: 10.1109/iSSSC50941.2020.9358890.
  9. Dr. S. Rama Krishna, Dr. S. V. Vasantha, K. Mani Deep, 2021, Survey on Fake News Detection using Machine learning Algorithms, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) ICACT – 2021 (Volume 09 – Issue 08),
  10. N. Baarir and A. Djeffal, "Fake News detection Using Machine Learning", 2020 2nd International Workshop on Human-Centric Smart Environments for Health and Well-being (IHSH), 2021.
  11. O. Ngada and B. Haskins, "Fake News Detection Using Content-Based Features and Machine Learning", 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), 2020.
  12. Kareem And S. M. Awan, "Pakistani Media Fake News Classification Using Machine Learning Classifiers," 2019 International Conference On Innovative Computing (Icic), 2019, Pp. 1-6, Doi: 10.1109/Icic48496.2019.8966734.
  13. Ahmad, M. Yousaf, S. Yousaf And M. Ahmad, "Fake News Detection Using Machine Learning Ensemble Methods", Complexity, Vol. 2020, Pp. 1-11, 2020, Doi:10.1155/2020/8885861.
  14. Irena And Erwin Budi Setiawan, "Fake News (Hoax) Identification On Social Media Twitter Using Decision Tree C4.5 Method", JurnalResti (RekayasaSistem Dan TeknologiInformasi), Vol. 4, No. 4, Pp. 711-716, 2020. Available: 10.29207/Resti.V4i4.2125.
  15. Jain, Anjali & Shakya, Avinash&Khatter, Harsh & Gupta, Amit. (2019). A smart System for Fake News Detection Using Machine Learning. 1-4. 10.1109/ICICT46931.2019.8977659
  16. Spector, A. Z. 1989. Achieving application requirements. In Distributed Systems, S. Mullender
Index Terms

Computer Science
Information Sciences

Keywords

Fake News Machine Learning SVM Random Forest Logistic Regression Naïve Bayes.

Powered by PhDFocusTM