The rise of technology and social media has altered the human cognition, and we must rethink our approach toward information dissemination systems when dealing with topics such as social campaigns, advertising, false news outbreak, and more. In this article, we start by providing an overview of classical information spread dynamics using various macroscopic models, including the famous Maki–Thompson model. Building on these, we propose and design context-aware modeling frameworks capable of capturing specific scenarios in online social media information spread. We propose four context-aware macroscopic models capable of capturing the dynamics of information diffusion for a specific context. We also present stochastic versions of these models. Case studies using real Twitter data, along with an algorithm to construct ignorant–spreader–recovered (ISR) groups are presented to validate the proposed models.

中文翻译：

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在复杂的社会技术系统中模拟社会传染和信息扩散

技术和社交媒体的兴起改变了人类的认知，在处理诸如社交活动，广告，虚假新闻爆发等话题时，我们必须重新考虑我们对信息传播系统的态度。在本文中，我们首先概述使​​用各种宏观模型（包括著名的Maki-Thompson模型）的经典信息传播动力学。在此基础上，我们提出并设计了上下文感知建模框架，该框架能够捕获在线社交媒体信息传播中的特定场景。我们提出了四个能够感知上下文的宏观模型，这些模型能够捕获特定上下文的信息传播动态。我们还介绍了这些模型的随机版本。使用真实的Twitter数据进行案例研究，