Systematic Review of Fake News, Propaganda, and Disinformation: Examining Authors, Content, and Social Impact Through Machine Learning
In recent years, the world has witnessed a global outbreak of fake news, propaganda and disinformation (FNPD) flows on online social networks (OSN). In the context of information warfare and the capabilities of generative AI, FNPDs have proliferated. They have become a powerful and quite effective t...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10843666/ |
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| author | Darius Plikynas Ieva Rizgeliene Grazina Korvel |
| author_facet | Darius Plikynas Ieva Rizgeliene Grazina Korvel |
| author_sort | Darius Plikynas |
| collection | DOAJ |
| description | In recent years, the world has witnessed a global outbreak of fake news, propaganda and disinformation (FNPD) flows on online social networks (OSN). In the context of information warfare and the capabilities of generative AI, FNPDs have proliferated. They have become a powerful and quite effective tool for influencing people’s social identities, attitudes, opinions and even behavior. Ad hoc malicious social media accounts and organized networks of trolls and bots target countries, societies, social groups, political campaigns and individuals. As a result, conspiracy theories, echo chambers, filter bubbles and other processes of fragmentation and marginalization are polarizing, radicalizing, and disintegrating society in terms of coherent politics, governance, and social networks of trust and cooperation. This systematic review aims to explore advances in using machine and deep learning to detect FNPD in OSNs effectively. We present the results of a combined PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) review in three analysis domains: 1) propagators (authors, trolls, and bots), 2) textual content, 3) social impact. This systemic research framework integrates meta-analyses of three research domains, providing an overview of the wider research field and revealing important relationships between these research domains. It not only addresses the most promising ML/DL research methodologies and hybrid approaches in each domain, but also provides perspectives and insights on future research directions. |
| format | Article |
| id | doaj-art-f01ef46d090747ad897693763f181bef |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-f01ef46d090747ad897693763f181bef2025-01-31T00:01:06ZengIEEEIEEE Access2169-35362025-01-0113175831762910.1109/ACCESS.2025.353068810843666Systematic Review of Fake News, Propaganda, and Disinformation: Examining Authors, Content, and Social Impact Through Machine LearningDarius Plikynas0https://orcid.org/0000-0002-5906-2324Ieva Rizgeliene1https://orcid.org/0009-0000-3211-5126Grazina Korvel2https://orcid.org/0000-0002-1931-6852Institute of Data Science and Digital Technologies, Faculty of Mathematics and Informatics, Vilnius University, Vilnius, LithuaniaInstitute of Data Science and Digital Technologies, Faculty of Mathematics and Informatics, Vilnius University, Vilnius, LithuaniaInstitute of Data Science and Digital Technologies, Faculty of Mathematics and Informatics, Vilnius University, Vilnius, LithuaniaIn recent years, the world has witnessed a global outbreak of fake news, propaganda and disinformation (FNPD) flows on online social networks (OSN). In the context of information warfare and the capabilities of generative AI, FNPDs have proliferated. They have become a powerful and quite effective tool for influencing people’s social identities, attitudes, opinions and even behavior. Ad hoc malicious social media accounts and organized networks of trolls and bots target countries, societies, social groups, political campaigns and individuals. As a result, conspiracy theories, echo chambers, filter bubbles and other processes of fragmentation and marginalization are polarizing, radicalizing, and disintegrating society in terms of coherent politics, governance, and social networks of trust and cooperation. This systematic review aims to explore advances in using machine and deep learning to detect FNPD in OSNs effectively. We present the results of a combined PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) review in three analysis domains: 1) propagators (authors, trolls, and bots), 2) textual content, 3) social impact. This systemic research framework integrates meta-analyses of three research domains, providing an overview of the wider research field and revealing important relationships between these research domains. It not only addresses the most promising ML/DL research methodologies and hybrid approaches in each domain, but also provides perspectives and insights on future research directions.https://ieeexplore.ieee.org/document/10843666/Machine learningdeep learningpropaganda and disinformationfake newsPRISMA systematic reviewauthors’ analysis |
| spellingShingle | Darius Plikynas Ieva Rizgeliene Grazina Korvel Systematic Review of Fake News, Propaganda, and Disinformation: Examining Authors, Content, and Social Impact Through Machine Learning IEEE Access Machine learning deep learning propaganda and disinformation fake news PRISMA systematic review authors’ analysis |
| title | Systematic Review of Fake News, Propaganda, and Disinformation: Examining Authors, Content, and Social Impact Through Machine Learning |
| title_full | Systematic Review of Fake News, Propaganda, and Disinformation: Examining Authors, Content, and Social Impact Through Machine Learning |
| title_fullStr | Systematic Review of Fake News, Propaganda, and Disinformation: Examining Authors, Content, and Social Impact Through Machine Learning |
| title_full_unstemmed | Systematic Review of Fake News, Propaganda, and Disinformation: Examining Authors, Content, and Social Impact Through Machine Learning |
| title_short | Systematic Review of Fake News, Propaganda, and Disinformation: Examining Authors, Content, and Social Impact Through Machine Learning |
| title_sort | systematic review of fake news propaganda and disinformation examining authors content and social impact through machine learning |
| topic | Machine learning deep learning propaganda and disinformation fake news PRISMA systematic review authors’ analysis |
| url | https://ieeexplore.ieee.org/document/10843666/ |
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