在基于K-means集群分析的社交媒体网络中优化了论传播系统
Mingchao Qi1, JunQiang Zhao1, Yan Feng1
1Xinxiang Medical University, Xinxiang, 453000, China.
Heliyon
|January 6, 2025
概括
本研究介绍了一种针对社交媒体的优化公众论监测模型,该模型结合了K-means集群和粒子集群优化 (PSO). 该模型在追踪论传播速度,范围,深度和情绪有效性方面表现出卓越的准确性.
科学领域:
- 社交网络分析 社交网络分析
- 计算社会科学 计算社会科学
- 数据挖掘 数据挖掘
背景情况:
- 有效的论监测对于理解社会趋势至关重要.
- 现有的社交媒体监控工具在分析复杂的传播模式时往往缺乏准确性.
- 优化聚类算法可以提高公众论分析的精度.
研究的目的:
- 开发和评估一种新的社会媒体论监测模型.
- 提高分析公众意见传播的准确性和有效性.
- 为了提高监控,利用K-意味着集群和粒子集群优化 (PSO).
主要方法:
- 整合K-平均数集群算法与粒子集群优化 (PSO).
- 使用社交媒体数据集对模型进行全面评估.
- 对传播速度,范围,深度和情绪有效性的分析.
主要成果:
- 拟议的模型在传播速度 (4.2-4.4),范围 (4.3-4.5) 和深度 (4.3-4.5) 中显示了高分.
- 情绪传播有效性得分在情绪倾向,极性和扩散方面从4.4到4.5不等.
- 参数灵敏度分析表明,聚类纯度 (0.822) 和兰德指数 (0.623) 的改善.
结论:
- 结合K-means和PSO模型,为社交媒体公众论监测提供了一个高效可靠的解决方案.
- 该模型在分析传播深度和情绪有效性方面取得了显著的改进.
- 这项研究为公众论分析和社交媒体研究提供了宝贵的见解.
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