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Comparison of Different Machine Learning Methods to Detect Fake News

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Innovations in Bio-Inspired Computing and Applications (IBICA 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 419))

Abstract

Information content that is inaccurate, misleading, or whose source cannot be verified is fake news. This content could be created to purposely harm people’s reputations, deceive them, or draw attention to themselves. Since December 2019, the epidemic of coronavirus disease has sparked considerable alarm and has had a significant impact on people’s lives. Also, misinformation on COVID-19 is frequently spread on social media. This project aims to use Machine learning algorithms to recognize fraudulent news. For this, we use seven essential algorithms, namely Logistic regression, Naïve Bayes, Support Vector Machine (SVM), Neural Network (NN), K-Nearest Neighbours (KNN), Decision tree, and Random forest. We compared the results of all the algorithms stated above and found that neural networks and random forest achieved the highest accuracy of 83%.

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Author information

Authors and Affiliations

  1. Computer Department, Pimpri Chinchwad College of Engineering, Pune, 411044, India

    Tanishka Badhe, Janhavi Borde, Vaishnavi Thakur, Bhagyashree Waghmare & Anagha Chaudhari

Authors
  1. Tanishka Badhe
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  2. Janhavi Borde
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  3. Vaishnavi Thakur
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  4. Bhagyashree Waghmare
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  5. Anagha Chaudhari
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Corresponding authors

Correspondence to Tanishka Badhe , Janhavi Borde , Vaishnavi Thakur , Bhagyashree Waghmare or Anagha Chaudhari .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Departamento de Engenharia Informática, Instituto Superior de Engenharia do Port, Porto, Portugal

    Ana Maria Madureira

  3. Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Vilnius, Lithuania

    Arturas Kaklauskas

  4. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  5. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Anu Bajaj

  6. Faculty of Information Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia

    Azah Kamilah Muda

  7. Vilnius University, Kaunas, Lithuania

    Dalia Kriksciuniene

  8. Lisbon University Institute, Lisbon, Portugal

    João Carlos Ferreira

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© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Cite this paper

Badhe, T., Borde, J., Thakur, V., Waghmare, B., Chaudhari, A. (2022). Comparison of Different Machine Learning Methods to Detect Fake News. In: Abraham, A., et al. Innovations in Bio-Inspired Computing and Applications. IBICA 2021. Lecture Notes in Networks and Systems, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-030-96299-9_7

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