EAGER: Collaborative: Understanding and Modeling Rumor Propagation for Vulnerability Assessment of Social Media Platforms

随着社交媒体成为主要的新闻来源，谣言可以在短时间内广泛传播。在最近的案例中，错误信息的迅速传播引起了社会恐慌，产生了巨大的经济影响，并使个人和社区处于巨大的风险之中。即使最终传播了正确的信息，大量的延误也会造成毁灭性的后果。为了确定网络对错误信息传播的脆弱性，并制定有效的主动和被动应对措施，有必要对谣言传播进行研究。然而，由于谣言传播的动态复杂性和自持性，对其建模和捕获具有挑战性。从本地（用户转发行为）和全球（网络扩散）的角度来看，谣言在在线社交网络中的快速传播受到许多因素的影响。扩散取决于每个用户对是否传播信息的本地决策。这一决定与信任关系、信息来源和内容等因素有关。传播还取决于网络的全局拓扑结构，用户如何相互连接，以及用户传播内容的速度。从这个全局的角度来看，表征谣言在网络上的传播需要准确而易于处理的传播数学模型。本项目从这两个角度来研究谣言在社交媒体上的传播。在这个项目中，社会心理学和计算机科学方法的整合揭示了大规模网络中的传播模式和驱动用户行为的心理动机。该项目有助于更好地监测、发现并最终防止破坏社会稳定和国家安全的错误信息的传播。研究和培训的机会提供给学生跨多个领域，包括计算机科学，工程和社会科学。

As social media becomes a primary news source, rumors can spread widely in a short time. In recent cases, the rapid spread of misinformation caused social panic, had a dramatic financial impact, and put individuals and communities at great risk. Even when correct information is eventually disseminated, large delays can have devastating consequences. To determine how vulnerable networks are to misinformation spread, and to develop effective proactive and reactive counter-measures, it is necessary to study rumor propagation. However, rumor propagation is challenging to model and capture due to its dynamic complexity and self-sustaining nature.The rapid diffusion of rumors across online social networks is influenced by numerous factors from both local (user forwarding behavior) and global (network diffusion) perspectives. Diffusion depends on each user's local decision about whether to propagate the information or not. That decision is related to factors including trust relationships, information provenance, and content. Diffusion also depends on the global topology of networks, how users are interconnected, as well as the rate at which users propagate the content. From this global point of view, characterizing rumor propagation across networks requires accurate yet tractable mathematical models of diffusion. This project investigates rumor diffusion via social media from these two perspectives. The integration of social psychological and computer science methodologies in this project reveals propagation patterns in large-scale networks and the psychological motivations driving user behavior. This project contributes to better monitoring, detection, and ultimately prevention of the propagation of misinformation that undermines social stability and national security. Research and training opportunities are offered to students across multiple fields, including computer science, engineering, and social science.