| Full text | |
| Author(s): |
Total Authors: 3
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| Affiliation: | [1] Univ Fed Fluminense UFF, Grad Program Elect & Telecommun Engn, BR-24230340 Niteroi, RJ - Brazil
Total Affiliations: 1
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| 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 |