在社交媒体上使用混合高斯混合模型的无监督假新闻检测
Sajida Perveen1, Muhammad Shahbaz2, Sami S Albouq3
1Department of computer Science, National Textile University, Faisalabad, Pakistan.
PloS one
|August 18, 2025
概括
这项研究介绍了一种无监督的假新闻检测方法,使用诸如高斯混合模型 (GMM) 这样的集群算法. 新的混合GMM方法与集团咨询优化器 (GCO) 显著提高了假新闻识别准确度,而无需手动标签.
科学领域:
- 计算社会科学 计算社会科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 社交媒体的快速增长在信息验证方面带来了挑战.
- 假新闻的大量传播侵蚀了新闻界的信誉,并助长了社会矛盾.
- 由于在线内容的压倒性数量,自动虚假新闻检测至关重要.
研究的目的:
- 开发一种无监督的假新闻检测方法,消除手动数据标签的需要.
- 评估集群算法 (GMM,K-means,K-medoids) 对于虚假新闻检测的有效性.
- 提出和验证一种新的混合GMM-GCO模型,用于增强假新闻识别.
主要方法:
- 使用集群算法的无监督学习:高斯混合模型 (GMM),K-means和K-medoids.
- 开发一种混合GMM方法,与集团咨询优化器 (GCO) 的元启发算法集成.
- 在真实数据集上,对集群性能进行比较分析,使用像轮得分,ARI和纯度等指标.
主要成果:
- 拟议的混合GMM-GCO方法与现有技术相比,表现优越.
- 获得了高集群质量评分:轮评分为0.77,ARI为0.83,纯度评分为0.88.
- 有效地消除了在假新闻检测中手动标记数据集的要求.
结论:
- 混合GMM-GCO方法为准确的假新闻检测提供了一个有希望的无监督解决方案.
- 这种方法解决了监督学习方法的局限性,消除了对大量数据注释的需求.
- 这些发现表明,自动虚假新闻检测系统取得了重大进展,提高了信息完整性.
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