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Published in:
The Methods and Approaches of Explainable Artificial Intelligence Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Fake or Real? The Novel Approach to Detecting Online Disinformation Based on Multi ML Classifiers

Authors : Martyna Tarczewska, Anna Marciniak, Agata Giełczyk

Published in: Computational Science – ICCS 2021

Publisher: Springer International Publishing

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Abstract

Background: the machine learning (ML) techniques have been implemented in numerous applications and domains, including health-care, security, entertainment, and sports. This paper presents how ML can be used for detecting fake news. The problem of online disinformation has recently become one of the most challenging issues of computer science. Methods: in this research, a fake news detection method based on multi classifiers (CNN, XGBoost, Random Forest, Naive Bayes, SVM) has been developed. In the proposed method, two classifiers cooperate; consequently, they obtain better results. Realistic, publicly available data was used in order to train and test the classifiers, Results: in the article, several experiments were presented; they differ in the implemented classifiers, and some improved parameters. Promising results (accuracy = 0.95, precision = 0.99, recall = 0.91, and F1-score = 0.95) were reported. Conclusion: the presented research proves that machine learning is a promising approach to fake news detection.

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Footnotes
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Metadata
Title
Fake or Real? The Novel Approach to Detecting Online Disinformation Based on Multi ML Classifiers
Authors
Martyna Tarczewska
Anna Marciniak
Agata Giełczyk
Copyright Year
2021
Book
Computational Science – ICCS 2021

Print ISBN: 978-3-030-77969-6

Electronic ISBN: 978-3-030-77970-2

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-77970-2_2

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