Lu Peng, Zhang Zhuo, Li Mengdi
Department of Sociology, Central South University, Changsha, China.
Center of Social System, Beijing Institute for General Artificial Intelligence, Beijing, China.
Complex Intell Systems. 2021;7(6):3259-3276. doi: 10.1007/s40747-021-00532-5. Epub 2021 Sep 17.
Under the mobile internet and big data era, more and more people are discussing and interacting online with each other. The forming process and evolutionary dynamics of public opinions online have been heavily investigated. Using agent-based modeling, we expand the Ising model to explore how individuals behave and the evolutionary mechanism of the life cycles. The big data platform of Douban.com is selected as the data source, and the online case "NeiYuanWaiFang" is applied as the real target, for our modeling and simulations to match. We run 10,000 simulations to find possible optimal solutions, and we run 10,000 times again to check the robustness and adaptability. The optimal solution simulations can reflect the whole life cycle process. In terms of different levels and indicators, the fitting or matching degrees achieve the highest levels. At the micro-level, the distributions of individual behaviors under real case and simulations are similar to each other, and they all follow normal distributions; at the middle-level, both discrete and continuous distributions of supportive and oppositive online comments are matched between real case and simulations; at the macro-level, the life cycle process (outbreak, rising, peak, and vanish) and durations can be well matched. Therefore, our model has properly seized the core mechanism of individual behaviors, and precisely simulated the evolutionary dynamics of online cases in reality.
在移动互联网和大数据时代，越来越多的人在网上相互讨论和互动。网络舆论的形成过程和演化动态已得到大量研究。我们使用基于主体的建模方法，扩展伊辛模型来探究个体行为方式以及生命周期的演化机制。选取豆瓣网的大数据平台作为数据源，并将网络事件“内圆外方”作为实际研究对象，以使我们的建模和模拟与之匹配。我们运行10000次模拟以寻找可能的最优解，然后再次运行10000次以检验其稳健性和适应性。最优解模拟能够反映整个生命周期过程。在不同层面和指标上，拟合度或匹配度均达到最高水平。在微观层面，实际案例和模拟中个体行为的分布彼此相似，且均服从正态分布；在中间层面，实际案例和模拟中支持和反对的网络评论的离散和连续分布相匹配；在宏观层面，生命周期过程（爆发、上升、峰值和消失）及持续时间能够很好地匹配。因此，我们的模型恰当地抓住了个体行为的核心机制，并精确模拟了现实中网络事件的演化动态。
