USING FEATURE ENGINEERING IN MACHINE LEARNING MODELS FOR FAKE NEWS DETECTION

Igor Olenych, M. Prytula, Ya. Boyko, O. Sinkevych, O. Khamar

Abstract


In this study, the analytical system for processing Ukrainian and Russian texts and automatically detecting fake news was developed. The effectiveness of text message classification using the naive Bayes classifier, support vector machine, k-nearest neighbors, random forest and logistic regression methods was studied. It has been established that adding to the feature vector the number of positive and negative words, the text tone, and the aggression presence makes it possible to increase the accuracy of detecting fake news for developed machine learning models. The methods of support vector machine and logistic regression demonstrate the highest effectiveness of text message classification.

Keywords: computer text analysis, fake detection, machine learning, feature engineering.


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References


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DOI: http://dx.doi.org/10.30970/eli.24.5

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