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Identification of Fake News Using Deep Neural Network-Based Hybrid Model

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Abstract

The current era of social digitization and exponential growth of social media decreases the distance between people to connect with each other. Due to increasing the massive information on social media networks such as Facebook, Twitter and Instagram etc., it also increases the fake/incorrect information among users. This fake/incorrect information may cause severe effects on various levels of society, i.e., individuals (depression, increased death rate etc.), political (influenced by some political party for harm/benefit), religion and society etc. In this paper, we initially proposed machine learning models as a baseline and, later, a hybrid deep learning model (CNN + LSTM) for detecting fake/real news on text and image datasets. Finally, a comparative analysis is conducted with state-of-the-art to validate the proposed model. The experimental evaluation reveals that the proposed model achieved 96.1% accuracy on text news and 91.36% on image news data.

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Authors and Affiliations

  1. Ajay Kumar Garg Engineering College, Ghaziabad, India

    Sonam Gupta, Bhanu Verma & Pradeep Gupta

  2. Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, India

    Lipika Goel

  3. National Institute of Technology, Hamirpur, HP, India

    Arun Kumar Yadav

  4. School of Computer and Information Sciences, IGNOU, Delhi, India

    Divakar Yadav

Authors
  1. Sonam Gupta
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  2. Bhanu Verma
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  3. Pradeep Gupta
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  4. Lipika Goel
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  5. Arun Kumar Yadav
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  6. Divakar Yadav
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Correspondence to Divakar Yadav.

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This article is part of the topical collection “Research Trends in Communication and Network Technologies” guest edited by Anshul Verma, Pradeepika Verma and Kiran Kumar Pattanaik.

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Gupta, S., Verma, B., Gupta, P. et al. Identification of Fake News Using Deep Neural Network-Based Hybrid Model. SN COMPUT. SCI. 4, 679 (2023). https://doi.org/10.1007/s42979-023-02117-0

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