In-Silico Sandbox to Model Multi-Platform Computational Disinformation

这项拟议的研究计划旨在推进社会网络和舆论动态建模的定量方法，解决关于通过社交媒体平台对虚假信息运动日益关注的研究差距。关于舆论动态的研究，特别是在多个社交媒体平台上传播“假新闻”的研究正在增长，但往往被社交媒体平台的发展所超越。现有文献的最新进展侧重于检测和描述在社交媒体上传播的虚假新闻，这留下了挑战和悬而未决的问题，包括算法干预在防止虚假信息传播方面的有效性。本研究项目旨在利用基于代理的建模技术，模拟多平台网络机制、社会互动和意见动态，创建具有统计代表性的加拿大社交媒体环境合成环境。重点是开发必要的新方法来创建和告知这种现实模拟器，以捕获潜在的复杂性和异质性，但仍可用于计算实验。该计划的短期目标是：1)建模并构建一个社交媒体环境的合成人口，该人口在统计上代表用户特征、意见和互动行为以及网络模式；2)开发计算算法，从经验数据中捕获和推算代理、网络和意见特征的分布；3)构建基于计算智能体的模型模拟器，统计再现社交媒体网络和观点的动态；4)创建一个硅沙盒来测试算法干预机制的效果。拟议项目的结果将是一个加拿大社交媒体环境的现实计算模拟器，该模拟器可以捕捉人类和社会行为的复杂性、意见动态以及跨多个社交媒体平台的复杂网络动态。这一提议将带来新的方法，为一个迫在眉睫的重要领域的应用提供信息——通过社交媒体话语的意见动态。我们开发的方法将有可能导致社会动力学方面的新发现。如果成功，该计算模拟器还将帮助告知社交媒体组织和公共机构制定干预措施，以防范虚假信息的幽灵。

This proposed research program aims to advance quantitative methods in modeling social networks and opinion dynamics, addressing a research gap about the growing concern around disinformation campaigns via social media platforms. Research on opinion dynamics, particularly the propagation of "false news" across multiple social media platforms, is growing but often outpaced by developments on social media platforms. Recent advances in extant literature focus on detecting and characterizing false news that propagates on social media, leaving challenges and open questions, including the efficacy of algorithmic interventions in preventing the spread of disinformation. This research program seeks to create a statistically representative synthetic environment of the Canadian social media environment using agent-based modeling techniques to simulate multi-platform network mechanisms, social interactions, and opinion dynamics. The focus is on developing new methods necessary to create and inform this realistic simulator that captures underlying complexity and heterogeneity but remains tractable for computational experimentation. The short-term objectives of the program are: 1) Model and construct a synthetic population of the social media environment that statistically represents user characteristics, opinions and interaction behavior, and network patterns; 2) Develop computational algorithms to capture and impute distributions of the agent, network, and opinion characteristics from empirical data; 3) Build a computational agent-based model simulator that statistically recreates the dynamics of social media networks and opinions endogenously; and 4) Create an in silico sandbox to test the effects of algorithmic intervention mechanisms. The outcome of the proposed program will be a realistic computational simulator of the Canadian social media environment that captures the complexity of human and social behavior, opinion dynamics, and complex network dynamics across multiple social media platforms. This proposal will lead to new methods that inform applications in an area of immediate importance - opinion dynamics via social media discourse. The methods we develop will have the potential to lead to novel discoveries in social dynamics. If successful, the proposed computational simulator will also help inform social media organizations and public institutions on developing interventions to guard against the specter of disinformation.