Liu Wenjia, Wang Jian, Ouyang Yanfeng
School of Management, Harbin Institute of Technology, Harbin, 150001 Heilongjiang China.
Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, 61801 Illinois USA.
Netw Spat Econ. 2022;22(4):831-854. doi: 10.1007/s11067-022-09574-9. Epub 2022 Jun 30.
This paper investigates rumor transmission over online social networks, such as those via Facebook or Twitter, where users liberally generate visible content to their followers, and the attractiveness of rumors varies over time and gives rise to opposition such as counter-rumors. All users in social media platforms are modeled as nodes in one of five compartments of a directed random graph: susceptible, hesitating, infected, mitigated, and recovered (SHIMR). The system is expressed with edge-based formulation and the transition dynamics are derived as a system of ordinary differential equations. We further allow individuals to decide whether to share, or disregard, or debunk the rumor so as to balance the potential gain and loss. This decision process is formulated as a game, and the condition to achieve mixed Nash equilibrium is derived. The system dynamics under equilibrium are solved and verified based on simulation results. A series of parametric analyses are conducted to investigate the factors that affect the transmission process. Insights are drawn from these results to help social media platforms design proper control strategies that can enhance the robustness of the online community against rumors.
本文研究谣言在在线社交网络中的传播，例如通过脸书或推特传播的谣言，在这些网络中，用户可以自由地向其关注者生成可见内容，并且谣言的吸引力会随时间变化，并引发诸如反谣言等反对声音。社交媒体平台中的所有用户都被建模为有向随机图的五个分区之一中的节点：易感、犹豫、感染、缓解和康复（SHIMR）。该系统用基于边的公式表示，过渡动态被推导为常微分方程组。我们进一步允许个体决定是分享、无视还是揭穿谣言，以便平衡潜在的收益和损失。这个决策过程被公式化为一个博弈，并推导了实现混合纳什均衡的条件。基于仿真结果求解并验证了均衡状态下的系统动态。进行了一系列参数分析，以研究影响传播过程的因素。从这些结果中得出见解，以帮助社交媒体平台设计适当的控制策略，从而增强在线社区抵御谣言的稳健性。
