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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

A Sensitive Stylistic Approach to Identify Fake News on Social Networking

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Author(s):
de Oliveira, Nicollas R. [1] ; Medeiros, Dianne S. V. [1] ; Mattos, Diogo M. F. [1]
Total Authors: 3
Affiliation:
[1] Univ Fed Fluminense UFF, Grad Program Elect & Telecommun Engn, BR-24230340 Niteroi, RJ - Brazil
Total Affiliations: 1
Document type: Journal article
Source: IEEE SIGNAL PROCESSING LETTERS; v. 27, p. 1250-1254, 2020.
Web of Science Citations: 0
Abstract

Human inefficiency to distinguish between true and false facts poses fake news as a threat to logical truth, which deteriorates democracy, journalism, and credibility in governmental institutions. In this letter, we propose a computational-stylistic analysis based on natural language processing, efficiently applying machine learning algorithms to detect fake news in texts extracted from social media. The analysis considers news from Twitter, from which approximately 33,000 tweets were collected, assorted between real and proven false. In assessing the quality of detection, 86% accuracy, and 94% precision stand out even employing a dimensional reduction to one-sixth of the number of original features. Our approach introduces a minimum overhead, while it has the potential of providing a high confidence index on discriminating fake from real news. (AU)

FAPESP's process: 18/23062-5 - MEGACHAIN: Blockchain for Integration, Privacy and Audit of Megacity Systems
Grantee:Célio Vinicius Neves de Albuquerque
Support type: Regular Research Grants