在没有底层网络结构的情况下,预测社交平台上的信息受欢迎程度
Leilei Wu1,2,3, Lingling Yi4, Xiao-Long Ren1
1Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China.
Entropy (Basel, Switzerland)
|June 28, 2023
概括
预测社交网络中的信息级联大小至关重要. 我们的新激活衰变算法使用早期转发数据准确预测内容的受欢迎程度,优于现有的方法.
科学领域:
- 社交网络分析 社交网络分析
- 信息传播建模 信息传播建模
- 计算社会科学 计算社会科学
背景情况:
- 预测信息级联的大小对于在线决策和病毒式营销至关重要.
- 传统的方法难以处理复杂的,多语言的数据或无法访问的网络结构.
- 现有的方法往往无法准确地捕捉跨平台传播的信息动态.
研究的目的:
- 开发一种新的算法,用于预测在线社交网络中信息布的大小.
- 解决处理各种在线内容和网络数据的传统方法的局限性.
- 提供基于早期参与的准确和有效的方法来预测基于早期参与的长期内容受欢迎程度.
主要方法:
- 在微信和微博社交网络平台的数据上进行实证研究.
- 开发一种基于激活-衰变 (AD) 的算法,利用早期的重置金额.
- 测试算法的适应传播趋势和预测长期动态的能力.
主要成果:
- 信息级联过程的特点是激活-衰变的动态.
- 基于AD的算法只使用早期的转发数据准确地预测了长期内容的受欢迎程度.
- 峰值和总信息传播之间存在强烈的相关性,提高了预测准确性.
- 拟议的方法在人气预测方面优于现有的基线方法.
结论:
- 激活-衰变模型为理解信息级联提供了一个强大的框架.
- 该AD算法提供了一个实用的解决方案,以高准确度预测在线内容的受欢迎程度.
- 确定峰值传播点显著提高了信息传播的预测能力.
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