基于因果关系的视觉分析,分析社交媒体主题中的情感传染
IEEE transactions on visualization and computer graphics
|December 8, 2025
概括
这项研究引入了一个新的框架和可视化工具CausalMap,以有效地绘制和理解情绪如何在社交媒体上跨主题传播. 它有助于分析复杂的关系和用户影响.
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
- 信息可视化 信息可视化
背景情况:
- 情感传染,即跨主题的态度传播,对于理解社交媒体动态至关重要.
- 现有的方法难以应对社交媒体的规模和复杂性,阻碍了高效的网络建设和深入分析.
- 挑战包括管理大量的主题和复杂的相互关系.
研究的目的:
- 开发一个高效的,基于因果关系的框架,用于构建和解释大规模的情绪传染网络.
- 介绍一种新的可视化技术,用于可扩展和直观地探索随时间推移的情感流动.
- 介绍CausalMap,一个用于追踪传染途径和评估人口影响的系统.
主要方法:
- 一个基于因果关系的框架,用于高效的情绪传染网络构建和解释.
- 一个类似地图的可视化,在水平轴上编码时间,以清晰地表示情绪流动.
- 开发用于路径追踪和影响评估的CausalMap系统.
主要成果:
- 拟议的框架和可视化技术使情绪传染网络的有效构建和探索成为可能.
- 原因地图有效地支持分析师追踪情绪路径和理解人口统计学影响.
- 包括用户研究和案例研究在内的全面评估验证了系统的可用性和有效性.
结论:
- 基于因果关系的框架和CausalMap可视化为分析社交媒体情绪传染提供了可扩展和有效的解决方案.
- 这种方法增强了对主题演变和社会影响力动态的理解.
- 这些发现对社会政策和对在线信息传播的分析有影响.
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