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足够但不要太多:行为扩散的双门模型
Fahimeh Alipour1, Fedor Dokshin2, Zeinab Maleki1
1Department of Electrical and Computer Engineering, Isfahan University of Technology, Khomeyni Shahr, Daneshgah e Sanati Hwy, Isfahan PG9G+39R, Isfahan, Iran.
PNAS nexus
|October 23, 2024
概括
双值模型与线性值模型不同,可以准确地预测行为采用和放弃. 这种新模式在理解社交媒体上的信息传播方面提供了显著的改进.
科学领域:
- 社交网络分析分析
- 信息传播动态信息传播动态
- 计算社会科学 计算社会科学
背景情况:
- 行为扩散通常是使用线性值模型来建模的,当足够多的社会联系人采用时,采用会发生.
- 经验观察表明,当太多的亲密接触者表现出这些行为时,个体也可能放弃这些行为,这种现象没有被线性模型所捕捉到.
研究的目的:
- 实证测试行为扩散的双值模型的有效性和预测能力.
- 调查一个上层值在触发行为不采用的作用.
主要方法:
- 扩展了基于决策树的算法,以在双值模型框架内估计异质值.
- 应用双门和线性门模型来分析用户对社交媒体新闻的参与,涉及三个不同的话题.
主要成果:
- 双值模型显示了预测准确度数量级大于线性值模型的用户参与度.
- 性能增长主要归因于双值模型在预测行为衰退方面的优越能力.
结论:
- 该研究证实了在某些社交媒体环境中影响信息传播的上限存在.
- 这些发现表明,双值机制可能与了解各种传播场景中的决策有关.
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