Big open-source social science: capabilities and methodology for automating social science analytics

Anthony Palladino; Elisa J. Bienenstock; Christopher A. George; Kendra E. Moore

doi:10.1117/12.2306500

27 April 2018 Big open-source social science: capabilities and methodology for automating social science analytics

Anthony Palladino, Elisa J. Bienenstock, Christopher A. George, Kendra E. Moore

Proceedings Volume 10653, Next-Generation Analyst VI; 106530D (2018) https://doi.org/10.1117/12.2306500
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

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.

Citation Download Citation

Anthony Palladino, Elisa J. Bienenstock, Christopher A. George, and 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); https://doi.org/10.1117/12.2306500

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
12 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Social sciences

Databases

Situational awareness sensors

Analytical research

Social network analysis

Analytics

Data processing

Show All Keywords

Keywords/Phrases

Search In:

Publication Years