Version 1
: Received: 31 October 2021 / Approved: 1 November 2021 / Online: 1 November 2021 (15:34:46 CET)
How to cite:
Donetski, K. Efficiencies of Feature Engineering in the Machine Learning approach for Fake News Classification. Preprints2021, 2021110024. https://doi.org/10.20944/preprints202111.0024.v1
Donetski, K. Efficiencies of Feature Engineering in the Machine Learning approach for Fake News Classification. Preprints 2021, 2021110024. https://doi.org/10.20944/preprints202111.0024.v1
Donetski, K. Efficiencies of Feature Engineering in the Machine Learning approach for Fake News Classification. Preprints2021, 2021110024. https://doi.org/10.20944/preprints202111.0024.v1
APA Style
Donetski, K. (2021). Efficiencies of Feature Engineering in the Machine Learning approach for Fake News Classification. Preprints. https://doi.org/10.20944/preprints202111.0024.v1
Chicago/Turabian Style
Donetski, K. 2021 "Efficiencies of Feature Engineering in the Machine Learning approach for Fake News Classification" Preprints. https://doi.org/10.20944/preprints202111.0024.v1
Abstract
The rapid infiltration of fake news is a flaw to the otherwise valuable internet, a virtually global network that allows for the simultaneous exchange of information. While a common, and normally effective, approach to such classification tasks is designing a deep learning-based model, the subjectivity behind the writing and production of misleading news invalidates this technique. Deep learning models are unexplainable in nature, making the contextualization of results impossible because it lacks explicit features used in traditional machine learning. This paper emphasizes the need for feature engineering to effectively address this problem: containing the spread of fake news at the source, not after it has become globally prevalent. Insights from extracted features were used to manipulate the text, which was then tested on deep learning models. The original unknown yet substantial impact that the original features had on deep learning models was successfully depicted in this study.
Fake news detection; Deep learning; Feature Engineering
Subject
Computer Science and Mathematics, Analysis
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
