Fake News Detection (Political Dataset)

Authors

  • Amina Qazi Department of Electronics and Telecommunication Engineering, Bharati Vidyapeeth College of Engineering, Navi Mumbai, India
  • Swapnil Anand Department of Electronics and Telecommunication Engineering, Bharati Vidyapeeth College of Engineering, Navi Mumbai, India
  • Amar Singh Department of Electronics and Telecommunication Engineering, Bharati Vidyapeeth College of Engineering, Navi Mumbai, India
  • Bhavya Shettigar Department of Electronics and Telecommunication Engineering, Bharati Vidyapeeth College of Engineering, Navi Mumbai, India
  • Shivam Shukla Department of Electronics and Telecommunication Engineering, Bharati Vidyapeeth College of Engineering, Navi Mumbai, India

Keywords:

BiLSTM, Deep Learning, R Language, Keras

Abstract

The increase in misleading information in everyday life through media outlets such as social feed, news blogs and online newspapers has made it very difficult to identify the trustworthy news sources, thus increasing the need for new technologies and computers to play the role by providing the better insights from data from online contents in online news. In this paper we have introduce the way BiLSTM algorithm can increase the chances of predicting news content accurately. In this paper we conduct a set of learning experiments to build accurate fake news detector. In addition, we have provided the analyses of the dataset.

Downloads

Download data is not yet available.

Downloads

Published

30-05-2021

Issue

Vol. 4 No. 5 (2021)

Section

Articles

License

Copyright (c) 2021 Amina Qazi, Swapnil Anand, Amar Singh, Bhavya Shettigar, Shivam Shukla

Creative Commons License

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

How to Cite

[1]
A. Qazi, S. Anand, A. Singh, B. Shettigar, and S. Shukla, “Fake News Detection (Political Dataset)”, IJRESM, vol. 4, no. 5, pp. 180–183, May 2021, Accessed: Apr. 26, 2024. [Online]. Available: https://journal.ijresm.com/index.php/ijresm/article/view/767