Comparative Performance of Machine Learning Algorithms for Fake News Detection

  • Arvinder Pal Singh BaliEmail author
  • Mexson Fernandes
  • Sourabh Choubey
  • Mahima Goel
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1046)


Automatic detection of fake news, which could negatively affect individuals and the society, is an emerging research area attracting global attention. The problem has been approached in this paper from Natural Language Processing and Machine Learning perspectives. The evaluation is carried out for three standard datasets with a novel set of features extracted from the headlines and the contents. Performances of seven machine learning algorithms in terms of accuracies and F1 scores are compared. Gradient Boosting outperformed other classifiers with mean accuracy of 88% and F1-Score of 0.91.


Fake news Natural Language Processing Text classification Machine learning algorithms Gradient boosting 



Comments on the paper by the anonymous reviewers were immensely helpful in revising the paper.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Arvinder Pal Singh Bali
    • 1
    Email author
  • Mexson Fernandes
    • 1
  • Sourabh Choubey
    • 1
  • Mahima Goel
    • 1
  1. 1.Asia Pacific Institute of Information Technology SD IndiaPanipatIndia

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