Text motifs: classifying influential individuals and follower networks with language agnostic methods

Elizabeth Bowman; Tod Hagan; Bruce McQueary; Rob Asfar; Jose Pascual

doi:10.1117/12.2307049

27 April 2018 Text motifs: classifying influential individuals and follower networks with language agnostic methods

Elizabeth Bowman, Tod Hagan, Bruce McQueary, Rob Asfar, Jose Pascual

Proceedings Volume 10653, Next-Generation Analyst VI; 106530J (2018) https://doi.org/10.1117/12.2307049
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

This paper discusses applying automated social media analytics to address challenges facing the OSINT analyst. The growing use and trust of social media presents significant potential for planning, executing, and assessing military information support operations (MISO). The enabling technology for this capability is called SURF, which is a GOTS tool developed to perform what is referred to as ‘group search’ by classifying individuals based on their network features and interactions. This language-agnostic tool has been validated at very high levels of accuracy for classifying users. This same technology can be used to support other critical MISO and commercial sector activities. In this paper, we present the background research, motivation for algorithm development, validation and example usage.

Conference Presentation

Citation Download Citation

Elizabeth Bowman, Tod Hagan, Bruce McQueary, Rob Asfar, and Jose Pascual "Text motifs: classifying influential individuals and follower networks with language agnostic methods", Proc. SPIE 10653, Next-Generation Analyst VI, 106530J (27 April 2018); https://doi.org/10.1117/12.2307049

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