Fake news detection algorithms – A systematic literature review
Social media and news platforms make available to their users, in real-time and simultaneously, access to a significant amount of content that may be true or false. It is remarkable that, with the evolution of Industry 4.0 technologies, the production and dissemination of fake news also increased in...
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| Vydáno v: | Data & knowledge engineering Ročník 158; s. 102441 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.07.2025
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| Témata: | |
| ISSN: | 0169-023X |
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| Abstract | Social media and news platforms make available to their users, in real-time and simultaneously, access to a significant amount of content that may be true or false. It is remarkable that, with the evolution of Industry 4.0 technologies, the production and dissemination of fake news also increased in recent years. Some content quickly reaches considerable popularity because it is accessed and shared on a large scale, especially in social networks, thus having a potential for going viral. Thus, this study aimed to identify the algorithms and software used for fake news detection. The choice for this combination is justified because in Brazil this process is carried out manually by verification agencies and thus, based on the mapping of the algorithms identified in the literature, an architecture proposal will be developed using artificial intelligence. As a methodology, a systematic literature review (SLR) was conducted in the Science Direct and Scopus databases using the keywords "fake news" and "machine learning" to locate reviews and research articles published in Engineering fields from 2018 to 2023. A total of 24 articles were analyzed, and the results pointed out that Facebook and X11Twitter has been renamed to X on July 23, 2023. were the social networks most used to disseminate fake news. Moreover, the main topics addressed were the COVID-19 pandemic and the United States presidential elections of 2016 and 2020. As for the most used algorithms, a predominance of neural networks was observed. The contribution of this study is in mapping the most used algorithms and their degree of assertiveness, as well as identifying the themes, countries and related researchers that help in the evolution of the fake news theme. |
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| AbstractList | Social media and news platforms make available to their users, in real-time and simultaneously, access to a significant amount of content that may be true or false. It is remarkable that, with the evolution of Industry 4.0 technologies, the production and dissemination of fake news also increased in recent years. Some content quickly reaches considerable popularity because it is accessed and shared on a large scale, especially in social networks, thus having a potential for going viral. Thus, this study aimed to identify the algorithms and software used for fake news detection. The choice for this combination is justified because in Brazil this process is carried out manually by verification agencies and thus, based on the mapping of the algorithms identified in the literature, an architecture proposal will be developed using artificial intelligence. As a methodology, a systematic literature review (SLR) was conducted in the Science Direct and Scopus databases using the keywords "fake news" and "machine learning" to locate reviews and research articles published in Engineering fields from 2018 to 2023. A total of 24 articles were analyzed, and the results pointed out that Facebook and X11Twitter has been renamed to X on July 23, 2023. were the social networks most used to disseminate fake news. Moreover, the main topics addressed were the COVID-19 pandemic and the United States presidential elections of 2016 and 2020. As for the most used algorithms, a predominance of neural networks was observed. The contribution of this study is in mapping the most used algorithms and their degree of assertiveness, as well as identifying the themes, countries and related researchers that help in the evolution of the fake news theme. |
| ArticleNumber | 102441 |
| Author | Richetti, Graziela Piccoli Knaesel, Vinícius Heinz Dal Forno, Ana Julia |
| Author_xml | – sequence: 1 givenname: Ana Julia orcidid: 0000-0003-2441-5385 surname: Dal Forno fullname: Dal Forno, Ana Julia email: ana.forno@ufsc.br organization: Professor at Postgraduate Program in Textile Engineering, Textile Engineering Department, Santa Catarina Federal University – UFSC campus Blumenau, Rua Eng. Udo Deeke, 485 - Bairro Salto do Norte, CEP 89065-100, Blumenau, SC, Brazil – sequence: 2 givenname: Graziela Piccoli orcidid: 0000-0001-9868-7768 surname: Richetti fullname: Richetti, Graziela Piccoli email: graziela.richetti@ufsc.br organization: Department of Exact Sciences and Education, Santa Catarina Federal University - UFSC campus Blumenau, Blumenau, SC, Brazil – sequence: 3 givenname: Vinícius Heinz orcidid: 0009-0007-2106-5187 surname: Knaesel fullname: Knaesel, Vinícius Heinz email: heinz.knaesel@gmail.com organization: Textile Engineer, Santa Catarina Federal University - UFSC campus Blumenau, Blumenau, SC, Brazil |
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| Cites_doi | 10.23860/JMLE-2018-10-2-7 10.1080/21670811.2017.1360143 10.1007/978-3-030-90087-8_1 10.1016/j.asoc.2021.107614 10.1016/j.knosys.2022.108378 10.1016/j.asoc.2020.107050 10.1016/j.comcom.2022.01.003 10.1016/j.asoc.2023.110125 10.1007/s11192-009-0146-3 10.1016/j.ipm.2021.102569 10.1016/j.compeleceng.2022.107967 10.1016/j.knosys.2022.109649 10.1109/IJCB48548.2020.9304909 10.1109/ACCESS.2021.3056079 10.1016/j.jnca.2021.103112 10.5585/2023.24970 10.1016/j.engappai.2023.106088 10.1016/j.compind.2018.06.004 10.1016/j.techfore.2019.05.021 10.5195/jmla.2022.1434 10.1016/j.asoc.2021.107360 10.1016/j.techsoc.2020.101454 10.1007/s42452-020-2326-y 10.1109/ACCESS.2021.3068659 10.1016/j.datak.2023.102182 10.1109/ACCESS.2024.3435497 10.1016/j.asoc.2021.107175 10.1016/j.asej.2023.102166 10.1016/j.asoc.2021.107559 10.1109/ACCESS.2021.3112806 |
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