This study presents an assessment of multiple approaches to determine the home and/or other important locations to a Twitter user. In this study, we present a unique approach to the problem of geotagged data sparsity in social media when performing geoinferencing tasks. Given the sparsity of explicitly geotagged Twitter data, the ability to perform accurate and reliable user geolocation from a limited number of geotagged posts has proven to be quite useful. In our survey, we have achieved accuracy rates of over 86% in matching Twitter user profile locations with their inferred home locations derived from geotagged posts.

This paper discusses the Knowledge Encapsulation Framework, a flexible, extensible evidence-marshalling environment built upon a natural language processing pipeline and exposed to users via an open-source semantic wiki. We focus our discussion on applications of the framework to intelligence and security applications, specifically, an instantiation of the KEF environment for researching illicit trafficking in nuclear materials.

Social Media provide an exciting and novel view into social phenomena. The vast amounts of data that can be gathered from the Internet coupled with massively parallel supercomputers such as the Cray XMT open new vistas for research. Conclusions drawn from such analysis must recognize that social media are distinct from the underlying social reality. Rigorous validation is essential. This paper briefly presents results obtained from computational analysis of social media - utilizing both blog and twitter data. Validation of these results is discussed in the context of a framework of established methodologies from the social sciences. Finally, an outline for a set of supporting studies is proposed.