27 April 2018 Big open-source social science: capabilities and methodology for automating social science analytics
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Currently, obtaining reliable situational awareness of the social landscape is an arduous, lengthy process involving manual analyses by social scientists. These traditional methods do not scale to the speed and diversity required by DoD operations or the high-speed, international business model in today’s corporate environment. Conversely, “big data” easily scales to meet these challenges but lacks the rigor of social science theory. We present Big Open-Source Social Science (BOSSS), a research and development project that leverages the strengths of social- and computer-science technology to address the operational need for rapid and reliable human-landscape situational-awareness. BOSSS iteratively filters, navigates, and summarizes diverse open-source data to characterize a local population’s social structure, conflicts, cleavages, affinities, and animosities. BOSSS automatically scrapes open-access data from the web and performs natural language processing to populate a knowledge graph with a custom schema. BOSSS then mines the graph to extract key, theory-agnostic socialscience principles of human inter-relations and dynamics: homophily, stratification, sentiment, and conflict. Automated quantitative social-network analysis provides up-to-date indicators of trends or anomalies within the local population’s social landscape. BOSSS’s emerging technology will provide a dramatic reduction in the cognitive workload for the next generation of analysts and will facilitate more rapid situational awareness both for deployed soldiers and private companies conducting operations abroad.
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Anthony Palladino, Anthony Palladino, Elisa J. Bienenstock, Elisa J. Bienenstock, Christopher A. George, Christopher A. George, Kendra E. Moore, Kendra E. Moore, } "Big open-source social science: capabilities and methodology for automating social science analytics", Proc. SPIE 10653, Next-Generation Analyst VI, 106530D (27 April 2018); doi: 10.1117/12.2306500; https://doi.org/10.1117/12.2306500


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