Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164231
Title: Topological data analysis helps to improve accuracy of deep learning models for fake news detection trained on very small training sets
Authors: Deng, Ran
Duzhin, Fedor
Keywords: Science::Mathematics
Issue Date: 2022
Source: Deng, R. & Duzhin, F. (2022). Topological data analysis helps to improve accuracy of deep learning models for fake news detection trained on very small training sets. Big Data and Cognitive Computing, 6(3). https://dx.doi.org/10.3390/bdcc6030074
Journal: Big Data and Cognitive Computing
Abstract: Topological data analysis has recently found applications in various areas of science, such as computer vision and understanding of protein folding. However, applications of topological data analysis to natural language processing remain under-researched. This study applies topological data analysis to a particular natural language processing task: fake news detection. We have found that deep learning models are more accurate in this task than topological data analysis. However, assembling a deep learning model with topological data analysis significantly improves the model’s accuracy if the available training set is very small.
URI: https://hdl.handle.net/10356/164231
ISSN: 2504-2289
DOI: 10.3390/bdcc6030074
Rights: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

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