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，谣言可能会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造成了社会恐慌，造成了巨大的金融影响，并将个人和社区置于极大的风险之中。即使最终传播了正确的信息，长时间的延误也可能造成毁灭性的后果。为了确定网络对错误信息传播的脆弱性，并制定有效的主动性和反应性对策，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，谣言传播的建模和捕获具有挑战性，谣言在在线社交网络中的快速传播受到来自局部(用户转发行为)和全局(网络扩散)两个角度的众多因素的影响。传播取决于每个用户关于是否传播信息的本地决定。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球拓扑结构、用户如何互连以及用户传播内容的速度。从这一全球角度来看，描述谣言在网络中传播的特征需要准确而又易于处理的传播数学模型。这个项目从这两个角度调查谣言在社交媒体上的传播。该项目融合了社会学、心理学和计算机科学的方法论，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。为包括计算机科学、工程和社会科学在内的多个领域的学生提供研究和培训机会。

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.