STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Computer Technology & Applications

Automated Fake News Detection System

  1. Pushpanjali S. Sajjanshetti
  2. Ashish Kumar Gupta
  3. Prince Kumar
  4. Ayush Gautam
  5. Ritik Kumar

Abstract

The classification of fake news on social media platforms has drawn a lot of attention recently because it is so simple to put false stuff there. In addition, they prefer social media to traditional television as their news source of choice. These patterns have increased academics' understanding of fake news and inspired an increase in interest in its identification. The primary goal of this work was to classify incorrect information found on text-based social media platforms (text classification). The false news dataset was categorized in this classification using ten different machine learning and deep learning classifiers. Four traditional techniques (term frequency-inverse document frequency, count vector, character level vector, and N-Gram level vector) were used to extract features from texts.

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