TY - GEN
T1 - Big open-source social science
T2 - Next-Generation Analyst VI 2018
AU - Palladino, Anthony
AU - Bienenstock, Elisa
AU - George, Christopher A.
AU - Moore, Kendra E.
N1 - Funding Information:
This work was sponsored by the Army Engineer Research and Development Center (ERDC) under contract No. W9132T-16-C-0016. Views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of ERDC or the U.S. Government. We would like to thank Mr. Timothy K. Perkins for his generous support, encouragement, and assistance throughout this research project.
Publisher Copyright:
© 2018 SPIE. Downloading of the abstract is permitted for personal use only.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Social situational awareness
KW - automated social science
KW - multi-modal data fusion
KW - social network analysis
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U2 - 10.1117/12.2306500
DO - 10.1117/12.2306500
M3 - Conference contribution
AN - SCOPUS:85049673329
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Next-Generation Analyst VI
A2 - Llinas, James
A2 - Hanratty, Timothy P.
PB - SPIE
Y2 - 16 April 2018 through 17 April 2018
ER -