一个在线社交网络的意见演变模型,考虑更高层次的互动
Quan Liu1,2, Yuekang Yao1, Meimei Jia1
1School of Artificial Intelligence and Software Engineering, Nanyang Normal University, Nanyang, China.
PloS one
|April 16, 2025
概括
本研究介绍了一种新的在线社交网络模型,其中包含了更高层次的交互,以更好地理解论动态. 研究结果表明,这些互动加快了信息的传播,并改善了共识形成,特别是在更大的群体中.
科学领域:
- 复杂的网络 复杂的网络
- 社交网络分析 社交网络分析
- 计算社会科学 计算社会科学
背景情况:
- 传统的复杂网络模型难以捕捉大型在线社交网络中信息和意见传播的复杂模式.
- 越来越多的用户基础需要更复杂的模型来准确地表示网络特征和动态.
研究的目的:
- 提出一个在线社交网络意见演变模型,整合了更高层次的互动.
- 分析群体互动和社会判断理论层面对意见动态的影响.
- 调查超边缘特征对信息传播和共识形成的影响.
主要方法:
- 开发一种意见演变模型,包括更高层次的相互作用和社会判断理论维度 (接受,不承诺,拒绝).
- 模拟使用邻居之间接受,中立和对比拒绝策略的意见交流动态.
- 数字模拟来分析超边缘大小和节点参与对信息传播和共识的影响.
主要成果:
- 高阶相互作用显著加快了信息传播的速度并扩大了信息传播的范围.
- 增加超边缘的平均大小有效地促进共识形成,当交互维度优化时.
- 仅仅增加参与的超边缘节点的数量对达成共识的影响有限.
结论:
- 高级互动对于准确建模在线社交网络中的意见演变至关重要.
- 拟议的模型为理解复杂的社会动态提供了理论和经验基础.
- 优化群体互动结构,而不仅仅是连接,是建立在线社区共识的关键.
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