在社交媒体平台上探索人类行为的时间不对称性
Liang Chen1, Heng Zheng1, Wenyue Wei1
1Department of Computer Science and Technology, College of Mathematics and Computer, Shantou University, Shantou 515063, China.
Entropy (Basel, Switzerland)
|January 28, 2026
概括
人类行为在关键时间段表现出不对称性,特别是在截止日期. 一个新的队列模型有效地捕捉了社交媒体上的这些时间模式.
科学领域:
- 信息科学 信息科学 信息科学
- 社交计算社会计算
- 行为经济学是一种行为经济学.
背景情况:
- 越来越多的人对理解复杂的人类行为,特别是行为不对称感兴趣.
- 行为不对称是一个重要的主题,具有理论和实际意义.
- 需要学术关注在关键时间段内的不对称性.
研究的目的:
- 在关键时间段调查人类行为的不对称性.
- 分析围绕关键时刻的社交媒体平台上的不对称的人类活动.
- 在社交媒体环境中检查截止日期影响的行为.
主要方法:
- 从杜班电影评论和微博评论中对数据集的全面分析.
- 专注于与重大产品发布活动相关的数据.
- 建议和应用一个队列模型,结合邻居效应和关键时间框架.
主要成果:
- 拟议的队列模型有效地捕捉了观察到的时间不对称性的模式.
- 证明了模型在反映截止日期影响的人类行为的准确性.
- 在关键时期,在社交媒体上发现了显著的不对称的人类活动.
结论:
- 队列模型是了解截止日期对社交媒体行为影响的宝贵工具.
- 人类行为表现出明显的不对称性,受到关键时间框架的影响.
- 进一步的研究可以在各种在线环境中探索这些动态.
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