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A Multiple change-point detection framework on linguistic characteristics of real versus fake news articles
Extracting information from textual data of news articles has been proven to be significant in developing efficient fake news detection systems. Pointedly, to fight disinformation, researchers concentrated on extracting information which focuses on exploiting linguistic characteristics that are comm...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10100634/ https://www.ncbi.nlm.nih.gov/pubmed/37055455 http://dx.doi.org/10.1038/s41598-023-32952-3 |
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author | Petrou, Nikolas Christodoulou, Chrysovalantis Anastasiou, Andreas Pallis, George Dikaiakos, Marios D. |
author_facet | Petrou, Nikolas Christodoulou, Chrysovalantis Anastasiou, Andreas Pallis, George Dikaiakos, Marios D. |
author_sort | Petrou, Nikolas |
collection | PubMed |
description | Extracting information from textual data of news articles has been proven to be significant in developing efficient fake news detection systems. Pointedly, to fight disinformation, researchers concentrated on extracting information which focuses on exploiting linguistic characteristics that are common in fake news and can aid in detecting false content automatically. Even though these approaches were proven to have high performance, the research community proved that both the language as well as the word use in literature are evolving. Therefore, the objective of this paper is to explore the linguistic characteristics of fake news and real ones over time. To achieve this, we establish a large dataset containing linguistic characteristics of various articles over the years. In addition, we introduce a novel framework where the articles are classified in specified topics based on their content and the most informative linguistic features are extracted using dimensionality reduction methods. Eventually, the framework detects the changes of the extracted linguistic features on real and fake news articles over the time incorporating a novel change-point detection method. By employing our framework for the established dataset, we noticed that the linguistic characteristics which concern the article’s title seem to be significantly important in capturing important movements in the similarity level of “Fake” and “Real” articles. |
format | Online Article Text |
id | pubmed-10100634 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-101006342023-04-14 A Multiple change-point detection framework on linguistic characteristics of real versus fake news articles Petrou, Nikolas Christodoulou, Chrysovalantis Anastasiou, Andreas Pallis, George Dikaiakos, Marios D. Sci Rep Article Extracting information from textual data of news articles has been proven to be significant in developing efficient fake news detection systems. Pointedly, to fight disinformation, researchers concentrated on extracting information which focuses on exploiting linguistic characteristics that are common in fake news and can aid in detecting false content automatically. Even though these approaches were proven to have high performance, the research community proved that both the language as well as the word use in literature are evolving. Therefore, the objective of this paper is to explore the linguistic characteristics of fake news and real ones over time. To achieve this, we establish a large dataset containing linguistic characteristics of various articles over the years. In addition, we introduce a novel framework where the articles are classified in specified topics based on their content and the most informative linguistic features are extracted using dimensionality reduction methods. Eventually, the framework detects the changes of the extracted linguistic features on real and fake news articles over the time incorporating a novel change-point detection method. By employing our framework for the established dataset, we noticed that the linguistic characteristics which concern the article’s title seem to be significantly important in capturing important movements in the similarity level of “Fake” and “Real” articles. Nature Publishing Group UK 2023-04-13 /pmc/articles/PMC10100634/ /pubmed/37055455 http://dx.doi.org/10.1038/s41598-023-32952-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Petrou, Nikolas Christodoulou, Chrysovalantis Anastasiou, Andreas Pallis, George Dikaiakos, Marios D. A Multiple change-point detection framework on linguistic characteristics of real versus fake news articles |
title | A Multiple change-point detection framework on linguistic characteristics of real versus fake news articles |
title_full | A Multiple change-point detection framework on linguistic characteristics of real versus fake news articles |
title_fullStr | A Multiple change-point detection framework on linguistic characteristics of real versus fake news articles |
title_full_unstemmed | A Multiple change-point detection framework on linguistic characteristics of real versus fake news articles |
title_short | A Multiple change-point detection framework on linguistic characteristics of real versus fake news articles |
title_sort | multiple change-point detection framework on linguistic characteristics of real versus fake news articles |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10100634/ https://www.ncbi.nlm.nih.gov/pubmed/37055455 http://dx.doi.org/10.1038/s41598-023-32952-3 |
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