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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationNext-Generation Analyst VI
EditorsJames Llinas, Timothy P. Hanratty
PublisherSPIE
Volume10653
ISBN (Electronic)9781510618176
DOIs
StatePublished - Jan 1 2018
EventNext-Generation Analyst VI 2018 - Orlando, United States
Duration: Apr 16 2018Apr 17 2018

Other

OtherNext-Generation Analyst VI 2018
CountryUnited States
CityOrlando
Period4/16/184/17/18

Fingerprint

Social sciences
Social Sciences
Open Source
methodology
situational awareness
Situational Awareness
Methodology
natural language processing
network analysis
Population Structure
Social Structure
Social Network Analysis
research projects
Business Model
Graph in graph theory
Electric network analysis
Stratification
stratification
Leverage
Research and Development

Keywords

  • automated social science
  • multi-modal data fusion
  • social network analysis
  • Social situational awareness

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

Palladino, A., Bienenstock, E., George, C. A., & Moore, K. E. (2018). Big open-source social science: Capabilities and methodology for automating social science analytics. In J. Llinas, & T. P. Hanratty (Eds.), Next-Generation Analyst VI (Vol. 10653). [106530D] SPIE. https://doi.org/10.1117/12.2306500

Big open-source social science : Capabilities and methodology for automating social science analytics. / Palladino, Anthony; Bienenstock, Elisa; George, Christopher A.; Moore, Kendra E.

Next-Generation Analyst VI. ed. / James Llinas; Timothy P. Hanratty. Vol. 10653 SPIE, 2018. 106530D.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Palladino, A, Bienenstock, E, George, CA & Moore, KE 2018, Big open-source social science: Capabilities and methodology for automating social science analytics. in J Llinas & TP Hanratty (eds), Next-Generation Analyst VI. vol. 10653, 106530D, SPIE, Next-Generation Analyst VI 2018, Orlando, United States, 4/16/18. https://doi.org/10.1117/12.2306500
Palladino A, Bienenstock E, George CA, Moore KE. Big open-source social science: Capabilities and methodology for automating social science analytics. In Llinas J, Hanratty TP, editors, Next-Generation Analyst VI. Vol. 10653. SPIE. 2018. 106530D https://doi.org/10.1117/12.2306500
Palladino, Anthony ; Bienenstock, Elisa ; George, Christopher A. ; Moore, Kendra E. / Big open-source social science : Capabilities and methodology for automating social science analytics. Next-Generation Analyst VI. editor / James Llinas ; Timothy P. Hanratty. Vol. 10653 SPIE, 2018.
@inproceedings{80e53b0f9d4747408243b3a7651182b6,
title = "Big open-source social science: Capabilities and methodology for automating social science analytics",
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.",
keywords = "automated social science, multi-modal data fusion, social network analysis, Social situational awareness",
author = "Anthony Palladino and Elisa Bienenstock and George, {Christopher A.} and Moore, {Kendra E.}",
year = "2018",
month = "1",
day = "1",
doi = "10.1117/12.2306500",
language = "English (US)",
volume = "10653",
editor = "James Llinas and Hanratty, {Timothy P.}",
booktitle = "Next-Generation Analyst VI",
publisher = "SPIE",

}

TY - GEN

T1 - Big open-source social science

T2 - Capabilities and methodology for automating social science analytics

AU - Palladino, Anthony

AU - Bienenstock, Elisa

AU - George, Christopher A.

AU - Moore, Kendra E.

PY - 2018/1/1

Y1 - 2018/1/1

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 - automated social science

KW - multi-modal data fusion

KW - social network analysis

KW - Social situational awareness

UR - http://www.scopus.com/inward/record.url?scp=85049673329&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85049673329&partnerID=8YFLogxK

U2 - 10.1117/12.2306500

DO - 10.1117/12.2306500

M3 - Conference contribution

AN - SCOPUS:85049673329

VL - 10653

BT - Next-Generation Analyst VI

A2 - Llinas, James

A2 - Hanratty, Timothy P.

PB - SPIE

ER -