Perveen Sajida, Shahbaz Muhammad, Albouq Sami S, Shinan Khlood, Alhazmi Hanan E, Alanazi Fatmah, Ashraf M Usman, Ashraf Rehan
Department of computer Science, National Textile University, Faisalabad, Pakistan.
Department of Computer Engineering, University of Engineering & Technology, Lahore, Pakistan.
PLoS One. 2025 Aug 18;20(8):e0330421. doi: 10.1371/journal.pone.0330421. eCollection 2025.
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.
社交媒体的兴起彻底改变了信息传播方式，既创造了新机遇，也带来了重大挑战。其中一个挑战就是假新闻的泛滥，这损害了新闻业的可信度，还导致社会动荡。由于内容数量庞大，人工识别假新闻不切实际，这促使人们开发用于检测假新闻的自动化系统。这一挑战激发了众多旨在开发假新闻检测自动化系统的研究工作。然而，这些方法大多依赖监督学习，构建标注数据集需要大量时间和精力。虽然有一些尝试开发用于假新闻检测的无监督方法，但报告的准确率结果仍不尽人意。本研究提出一种使用聚类算法的无监督方法，包括高斯混合模型（GMM）、K均值和K中心点算法，以在检测假新闻时无需人工标注。特别是，它还提出了一种新颖的混合方法，该方法将高斯混合模型（GMM）与一种元启发式优化算法——群体咨询优化器（GCO）结合使用，以确定用于检测假新闻的最佳聚类数。对真实世界数据评估结果的对比分析表明，所提出的混合GMM优于现有技术，轮廓系数为0.77，ARI为0.83，纯度分数为0.88，表明聚类结果质量有显著提高。
