Abstract
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.
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Bali, A.P.S., Fernandes, M., Choubey, S., Goel, M. (2019). Comparative Performance of Machine Learning Algorithms for Fake News Detection. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T., Kashyap, R. (eds) Advances in Computing and Data Sciences. ICACDS 2019. Communications in Computer and Information Science, vol 1046. Springer, Singapore. https://doi.org/10.1007/978-981-13-9942-8_40
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