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在线公众论热量的量化和演变 考虑交互行为和情绪冲突
Zhengyi Sun1, Deyao Wang2, Zhaohui Li3
1Graduate School of Information, Production and Systems, Waseda University, Kitakyushu 8080135, Japan.
Entropy (Basel, Switzerland)
|July 29, 2025
概括
这项研究引入了一个新的框架,通过整合互动和情绪因素来衡量在线论热度. 它确定了三个不同的事件传播模式:极端爆发,正常爆发和长尾.
科学领域:
- 计算社会科学 计算社会科学
- 网络科学 网络科学
- 数据挖掘 数据挖掘
背景情况:
- 互联网的快速增长加速了公共事件的传播和在线意见的传播.
- 目前用于量化公众论热量的现有方法往往忽略了诸如用户交互和情绪动态等关键因素.
- 这种差距阻碍了在事件传播期间对意见波动的准确分析.
研究的目的:
- 开发一个全面的框架来量化和分析在线论热量的动态演变.
- 通过结合交互行为和情绪情绪分析来解决当前方法的局限性.
- 将在线论事件分类为不同的传播模式.
主要方法:
- 引入了"相互作用热",使用信息获取比重对不同相互作用属性的权重.
- 使用SnowNLP扩展情感挖掘以捕获基于情感倾向变异的"情感热度".
- 集成的交互和情感数据用于全面的热量指数,采用霍德里克-普雷斯科特波器和K-means集群用于模式分类.
主要成果:
- 开发了一个新的框架,整合了互动和情绪热度,用于动态的公众论分析.
- 鉴定和分类了三个不同的传播模式:极端爆发,正常爆发和长尾事件.
- 量化了外部干预对意见传播趋势的影响.
结论:
- 拟议的框架有效量化和分析在线论热度,捕捉复杂的传播动态.
- 经验分析揭示了意见传播的关键特征,包括爆炸性,持久性和多轮波动.
- 这项研究强调了互动,情绪和外部因素在塑造公众论的相互作用.
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