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建模与分析相关信息共传播的跨传播动力学。

Modeling and analyzing cross-transmission dynamics of related information co-propagation.

机构信息

Communication University of China, Beijing, 100024, China.

York University, Toronto, M3J1P3, Canada.

出版信息

Sci Rep. 2021 Jan 11;11(1):268. doi: 10.1038/s41598-020-79503-8.

Abstract

The dissemination of one public hot event is usually affected by some related information, and the implication of co-propagation by different information is critical for the integrated analysis. To help in designing effective communication strategies during the whole event, we propose the cross-transmission susceptible-forwarding-immune (CT-SFI) model to describe the dynamics of co-propagation particularly with focus on the cross-transmission effects. This model is based on the forwarding quantity and takes into account the behavior that users may have a strong attraction or continuous attraction within or without an active time after contacting one information. Data fitting using the real data of Chinese Sina-microblog can accurately parameterize the model and parameter sensitivity analysis gives some strategies for co-propagation.

摘要

一个公共热点事件的传播通常会受到一些相关信息的影响,不同信息的共同传播的含义对于综合分析至关重要。为了帮助在整个事件中设计有效的传播策略,我们提出了交叉传播易感性-前向免疫(CT-SFI)模型来描述共同传播的动态,特别关注交叉传播的影响。该模型基于前向数量,并考虑了用户在接触一条信息后在活跃时间内或之外可能具有强烈吸引力或持续吸引力的行为。使用中国新浪微博的真实数据进行数据拟合,可以准确地参数化模型,而参数敏感性分析为共同传播提供了一些策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a5d2/7801523/dfd5a61af277/41598_2020_79503_Fig1_HTML.jpg

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