利用标签的力量:在社交媒体上挖掘时间模式和构建故事情节
Jinbao Song1,2, Yu He1,3, Xingyu Zhang1,3
1State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, P.R.China.
PloS one
|August 28, 2025
概括
通过分析标签时间模式和语义相关性, RoMLP-AttNet模型增强了事件分类,提高了回忆和精度,以更好地追踪公众意见.
科学领域:
- 社交媒体分析
- 自然语言处理
- 信息科学
背景情况:
- 微博是中国主要的社交媒体平台,
- 有效的事件时间表构建对于追踪公众意见和事件进展至关重要.
- 现有的方法难以应对社交媒体数据的规模和复杂性.
研究的目的:
- 提出一个框架来系统地模拟微博上的事件演变.
- 通过分析时间模式和标签的语义相关性来构建清晰有序的事件情节.
- 介绍和评估RoMLP-AttNet模型以改善微博事件分类.
主要方法:
- 时间特征提取以捕捉微博发布时间.
- 将时间信息与主题标签相似性结合起来进行相关性分析.
- 一个基于时间线的主题合并算法用于故事情节的构建.
- 使用主题发布序列进行事件检测的RoMLP-AttNet模型.
主要成果:
- 提出的框架成功地为"日本核废物"事件创造了一个清晰而完整的故事情节.
- 在微博事件分类方面, RoMLP-AttNet模型取得了显著的改进.
- 在回忆中平均提高了16.73%,精度提高了15.8%,F1得分提高了17.38%.
结论:
- 开发的框架有效地模拟事件演变,并从微博数据中构建事件故事.
- 在微博事件分类方面, RoMLP-AttNet模型提供了实质性的进展.
- 这项研究为分析和理解社交媒体事件动态提供了有价值的工具.
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