International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
권호 표지
DOI QR코드

DOI QR Code

Fake News Detector using Machine Learning Algorithms

  • Diaa Salama (Misr International University, Faculty of Computers and Informatics) ;
  • yomna Ibrahim (Misr International University, Faculty of Computers and Informatics) ;
  • Radwa Mostafa (Misr International University, Faculty of Computers and Informatics) ;
  • Abdelrahman Tolba (Misr International University, Faculty of Computers and Informatics) ;
  • Mariam Khaled (Misr International University, Faculty of Computers and Informatics) ;
  • John Gerges (Misr International University, Faculty of Computers and Informatics) ;
  • Diaa Salama (Misr International University, Faculty of Computers and Informatics)
  • Received : 2024.07.05
  • Published : 2024.07.30
Download PDF
⟨ Previous Next ⟩

Abstract

With the Covid-19(Corona Virus) spread all around the world, people are using this propaganda and the desperate need of the citizens to know the news about this mysterious virus by spreading fake news. Some Countries arrested people who spread fake news about this, and others made them pay a fine. And since Social Media has become a significant source of news, .there is a profound need to detect these fake news. The main aim of this research is to develop a web-based model using a combination of machine learning algorithms to detect fake news. The proposed model includes an advanced framework to identify tweets with fake news using Context Analysis; We assumed that Natural Language Processing(NLP) wouldn't be enough alone to make context analysis as Tweets are usually short and do not follow even the most straightforward syntactic rules, so we used Tweets Features as several retweets, several likes and tweet-length we also added statistical credibility analysis for Twitter users. The proposed algorithms are tested on four different benchmark datasets. And Finally, to get the best accuracy, we combined two of the best algorithms used SVM ( which is widely accepted as baseline classifier, especially with binary classification problems ) and Naive Base.

Keywords

References

  1. Atodiresei, C., Ta˘na˘selea, A., amp; Iftene, A. (2018). Identify- ing Fake News and Fake Users on Twitter. Procedia Computer Science, 126, 451-461. doi:10.1016/j.procs.2018.07.279.
  2. Jang, Y., Park, C., amp; Seo, Y. (2019). Fake News Analysis Modeling Using Quote Retweet. Electronics, 8(12), 1377. doi:10.3390/electronics8121377
  3. Author, F., Author, S.: Title of a proceedings paper. In: Editor, F., Editor, S. (eds.) CONFERENCE 2016, LNCS, vol. 9999, pp. 1-13. Springer, Heidelberg (2016).
  4. Author, F., Author, S., Author, T.: Book title. 2nd edn. Publisher, Location (1999).
  5. Author, F.: Contribution title. In: 9th International Proceedings on Proceedings, pp. 1-2. Publisher, Location (2010).
  6. LNCS Homepage, http://www.springer.com/lncs, last accessed 2016/11/21.
  7. Yurtoglu, N. (2018). Http Ajao, O., Bhowmik, D., and; Zargari, S. (2018). Fake News Identification on Twitter with Hybrid CNN and RNN Models. Proceedings of the 9th International Conference on Social Media and Society. doi:10.1145/3217804.3217917
  8. Gilda, S. (2017). Notice of Violation of IEEE Publication Principles: Evaluating machine learning algorithms for fake news detection. 2017 IEEE 15th Student Conference on Research and Development (SCOReD). doi:10.1109/scored.2017.8305411
  9. Krishnan, S., amp; Chen, M. (2018). Identifying Tweets with Fake News. 2018 IEEE International Conference on Informa tion Reuse and Integration (IRI). doi:10.1109/iri.2018.00073
  10. Kaliyar, R. K. (2018). Fake News Detection Using A Deep Neural Network. 2018 4th International Conference on Computing Communication and Automation (ICCCA). doi:10.1109/ccaa.2018.8777343
  11. Jain, A., amp; Kasbe, A. (2018). Fake News Detection. 2018 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS). doi:10.1109/sceecs.2018.8546944
  12. Povoda, L., Burget, R., amp; Dutta, M. K. (2016). Sentiment analysis based on Support Vector Machine and Big Data. 2016 39th International Conference on Telecommunications and Signal Processing (TSP). doi:10.1109/tsp.2016.7760939
  13. Girgis, S., Amer, E., amp; Gadallah, M. (2018). Deep Learning Algorithms for Detecting Fake News in Online Text. 2018 13th International Conference on Computer Engineering and Systems (ICCES). doi:10.1109/icces.2018.8639198