Speaker: Kathleen M. Carley, Carnegie Mellon University
Abstract: Our ability to understand and predict socio-cultural activity is being transformed by the exponential growth in big data available on the web – both social media data as well as open government and organizational records. Analysis of such data has the potential to create the timely and detailed information needed to improve crisis response and so save lives and goods, improve community resilience, support early identification of security threats and decrease social-cyber attacks. Across all these areas there are a set of common key methodological challenges are driven by the nature of the data: “wide” data, sampled data, and geo-temporal evolving data. These data challenges mean that despite there being more data than ever before, our ability to extract meaningful information and make predictions is limited, perhaps increasingly so, by the growth of false information, the trust that is placed in the analytical results, and the need for improved scalable technologies for assessing dense dynamic networks. In this presentation the promise of the new big data science for social behavior is described as well as the challenges that need to be considered from a network science perspective. These point will be illustrated using a variety of examples related to early tsunami warning in Indonesia, crisis response in Libya, global cyber security, and threat group detection.