论文标题
深度代理：研究社交网络中信息传播和演变的动态
Deep Agent: Studying the Dynamics of Information Spread and Evolution in Social Networks
论文作者
论文摘要
本文解释了根据DARPA的SocialSim计划开发的社交网络分析框架的设计，其新颖的体系结构对人类的情感，认知和社会因素进行了建模。我们的框架既是理论和数据驱动的，又利用领域专业知识。我们的模拟工作有助于了解信息如何在社交媒体平台中流动和发展。我们专注于建模三个信息域：针对三个相互关联的社交环境的加密货币，网络威胁和软件漏洞：GitHub，Reddit和Twitter。我们参加了2018年12月的Socialsim DARPA挑战赛，在该挑战中，我们的模型经过了广泛的绩效评估，以获得准确性，可推广性，解释性和实验能力。本文报告了我们的社交媒体建模工作中使用的主要概念和模型，用于在用户，社区，人群和内容级别开发多分辨率模拟。
This paper explains the design of a social network analysis framework, developed under DARPA's SocialSim program, with novel architecture that models human emotional, cognitive and social factors. Our framework is both theory and data-driven, and utilizes domain expertise. Our simulation effort helps in understanding how information flows and evolves in social media platforms. We focused on modeling three information domains: cryptocurrencies, cyber threats, and software vulnerabilities for the three interrelated social environments: GitHub, Reddit, and Twitter. We participated in the SocialSim DARPA Challenge in December 2018, in which our models were subjected to extensive performance evaluation for accuracy, generalizability, explainability, and experimental power. This paper reports the main concepts and models, utilized in our social media modeling effort in developing a multi-resolution simulation at the user, community, population, and content levels.
