College of Information and Communication Engineering, Communication University of China, Beijing, China.
Fields-CQAM Laboratory of Mathematics for Public Health, Laboratory for Industrial and Applied Mathematics, York University, Toronto, ON, Canada.
J Med Internet Res. 2021 Feb 12;23(2):e25734. doi: 10.2196/25734.
In a fast-evolving public health crisis such as the COVID-19 pandemic, multiple pieces of relevant information can be posted sequentially on a social media platform. The interval between subsequent posting times may have a different impact on the transmission and cross-propagation of the old and new information that results in a different peak value and a final size of forwarding users of the new information, depending on the content correlation and whether the new information is posted during the outbreak or quasi-steady-state phase of the old information.
This study aims to help in designing effective communication strategies to ensure information is delivered to the maximal number of users.
We developed and analyzed two classes of susceptible-forwarding-immune information propagation models with delay in transmission to describe the cross-propagation process of relevant information. A total of 28,661 retweets of typical information were posted frequently by each opinion leader related to COVID-19 with high influence (data acquisition up to February 19, 2020). The information was processed into discrete points with a frequency of 10 minutes, and the real data were fitted by the model numerical simulation. Furthermore, the influence of parameters on information dissemination and the design of a publishing strategy were analyzed.
The current epidemic outbreak situation, epidemic prevention, and other related authoritative information cannot be timely and effectively browsed by the public. The ingenious use of information release intervals can effectively enhance the interaction between information and realize the effective diffusion of information. We parameterized our models using real data from Sina Microblog and used the parameterized models to define and evaluate mutual attractiveness indexes, and we used these indexes and parameter sensitivity analyses to inform optimal strategies for new information to be effectively propagated in the microblog. The results of the parameter analysis showed that using different attractiveness indexes as the key parameters can control the information transmission with different release intervals, so it is considered as a key link in the design of an information communication strategy. At the same time, the dynamic process of information was analyzed through index evaluation.
Our model can carry out an accurate numerical simulation of information at different release intervals and achieve a dynamic evaluation of information transmission by constructing an indicator system so as to provide theoretical support and strategic suggestions for government decision making. This study optimizes information posting strategies to maximize communication efforts for delivering key public health messages to the public for better outcomes of public health emergency management.
在 COVID-19 大流行等快速演变的公共卫生危机中，社交媒体平台上可以连续发布多条相关信息。后续发布时间间隔可能会对新旧信息的传播和交叉传播产生不同的影响，从而导致新信息的转发用户的峰值和最终规模不同，具体取决于内容相关性以及新信息是在旧信息爆发阶段还是准稳态阶段发布。
本研究旨在帮助设计有效的沟通策略，以确保将信息传递给最多数量的用户。
我们开发并分析了具有延迟传输的两类易感染-转发-免疫信息传播模型，以描述相关信息的交叉传播过程。与 COVID-19 相关的具有高影响力的每个意见领袖（数据采集截至 2020 年 2 月 19 日）频繁发布了 28661 条典型信息的转发。这些信息被处理成离散点，频率为 10 分钟，通过模型数值模拟对真实数据进行拟合。此外，还分析了参数对信息传播的影响和发布策略的设计。
当前，公众无法及时有效地浏览有关疫情爆发情况、疫情防控等方面的权威信息。巧妙利用信息发布间隔，可以有效增强信息之间的互动，实现信息的有效扩散。我们使用来自新浪微博的真实数据对模型进行参数化，并使用参数化模型定义和评估相互吸引力指标，然后使用这些指标和参数敏感性分析来为微博中有效传播新信息提供最佳策略。参数分析结果表明，使用不同的吸引力指标作为关键参数，可以控制具有不同发布间隔的信息传输，因此可以将其视为信息通信策略设计的关键环节。同时，通过指标评估分析了信息的动态过程。
我们的模型可以对不同发布间隔的信息进行准确的数值模拟，并通过构建指标系统对信息传输进行动态评估，从而为政府决策提供理论支持和战略建议。本研究通过优化信息发布策略，将政府的努力最大化，以向公众传递关键的公共卫生信息，从而改善公共卫生应急管理的结果。
