Feature-Based Learning Model for Fake News Detection and Classification

Authors

  • G. Purna Chandar Rao  Research Scholar, Department of Computer Science and Engineering, University College of Engineering, Osmania University, Hyderabad, Telangana, India
  • V. B. Narasimha  Assistant Professor, Department of Computer Science and Engineering, University College of Engineering, Osmania University, Hyderabad, Telangana, India

DOI:

https://doi.org/10.32628/IJSRST2184111

Keywords:

Buzz News, Dropout layer, Fake news, Long Short Term Network Model, Social media

Abstract

A social media adoption is important to provide content authenticity and awareness for the unknown news that might be fake. Therefore, a Natural Language Processing (NLP) model is required to identify the content properties for language-driven feature generation. The present research work utilizes language-driven features that extract the grammatical, sentimental, syntactic, readable features. The feature from the particular news content is extracted to deal with the dimensional problem as the language level features are quite complex. Thus, the Dropout layer-based Long Short Term Network Model (LSTM) for sequential learning achieved better results during fake news detection. The results obtained validate the important features extracted linguistic model features and are combined to achieve better classification accuracy. The proposed Drop out based LSTM model obtained accuracy of 95.3% for fake news classification and detection when compared to the sequential neural model for fake news detection.

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International Journal of Scientific Research in Science and Technology

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Published

2021-11-30

Issue

Volume 8, Issue 6, November-December-2021

Section

Research Articles

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

How to Cite

[1]
G. Purna Chandar Rao, V. B. Narasimha "Feature-Based Learning Model for Fake News Detection and Classification" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 8, Issue 6, pp.130-139, November-December-2021. Available at doi : https://doi.org/10.32628/IJSRST2184111