Unsupervised fake news detection on social media using hybrid Gaussian Mixture Model Article Swipe
Sajida Perveen
,
Muhammad Shahbaz
,
Sami Albouq
,
Khlood Shinan
,
Hanan E. Alhazmi
,
Fatmah Alanazi
,
Muhammad Usman Ashraf
,
Rehan Ashraf
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0330421
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0330421
The rise of social media has revolutionized information dissemination, creating new opportunities but also significant challenges. One such challenge is the proliferation of fake news, which undermines the credibility of journalism and contributes to societal unrest. Manually identifying fake news is impractical due to the vast volume of content, prompting the development of automated systems for fake news detection. This challenge has motivated numerous research efforts aimed at developing automated systems for fake news detection. However, most of these approaches rely on supervised learning, which requires significant time and effort to construct labeled datasets. While there have been a few attempts to develop unsupervised methods for fake news detection, their reported accuracy results thereof remain unsatisfactory. This research proposes an unsupervised approach using clustering algorithms, including Gaussian Mixture Model (GMM), K-means, and K-medoids, to eliminate the need for manual labeling in detecting fake news. In particular, it also proposes a novel hybrid method that leverages the Gaussian Mixture Model (GMM) in conjunction with the Group Counseling Optimizer (GCO), a metaheuristic optimization algorithm, to identify the optimal number of clusters for the detection of fake news. The comparative analysis of the evaluation results on real-world data demonstrated that the proposed hybrid GMM outperforms the state-of-the-art techniques, with a silhouette score of 0.77, ARI of 0.83, and a purity score of 0.88, indicating a significantly improved quality of clustering results.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0330421
- https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0330421&type=printable
- OA Status
- gold
- References
- 131
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413272487
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Raw OpenAlex JSON
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https://openalex.org/W4413272487Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1371/journal.pone.0330421Digital Object Identifier
- Title
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Unsupervised fake news detection on social media using hybrid Gaussian Mixture ModelWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-08-18Full publication date if available
- Authors
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Sajida Perveen, Muhammad Shahbaz, Sami Albouq, Khlood Shinan, Hanan E. Alhazmi, Fatmah Alanazi, Muhammad Usman Ashraf, Rehan AshrafList of authors in order
- Landing page
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https://doi.org/10.1371/journal.pone.0330421Publisher landing page
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https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0330421&type=printableDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0330421&type=printableDirect OA link when available
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Mixture model, Computer science, Social media, Cluster analysis, Credibility, Artificial intelligence, Machine learning, Fake news, Heuristic, Pattern recognition (psychology), Data mining, World Wide Web, Political science, Law, Internet privacyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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