Application of confidence intervals to text based social network construction
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Application of Confidence Intervals to Text-based Social Network Construction. By CDT Julie Jorgensen, 06, G4 Advisors: MAJ Ian McCulloh, D/MATH LTC John Graham, D/BS&L. Agenda. The Real-World Problem Text Analysis/Social Network Analysis Solution Social Network Analysis

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Application of Confidence Intervals to Text-based Social Network Construction

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Application of confidence intervals to text based social network construction

Application of Confidence Intervals to Text-based Social Network Construction

By

CDT Julie Jorgensen, 06, G4

Advisors: MAJ Ian McCulloh, D/MATH

LTC John Graham, D/BS&L


Agenda

Agenda

  • The Real-World Problem

  • Text Analysis/Social Network Analysis Solution

    • Social Network Analysis

    • Simple Text Analysis

  • A Better Solution

    • Themed Analysis

    • Example Case – Jihadist Texts

    • Theme Scores

  • Network Construction Procedure

    • Jihadist Network

  • Results

  • Importance and Conclusions


The real world problem

The Real-World Problem

  • Commanders need to understand “Human Terrain”

  • Majority of ‘HT’ information is in text form

    • The Combating Terrorism Center receives volumes of data every day.

    • Harmony Database is being rapidly declassified

  • Need an efficient way to plow through large amounts of text data and see the linkages.

  • Solution: Text Analysis Displayed in Social Network Analysis


Social network analysis

Social Network Analysis

  • A mathematical method of quantifying connections between individuals or groups and drawing conclusions from those connections

  • Assumes rational beings are interdependent

    • Nodes

      • Key Actors

    • Links

      • Relationships between Nodes


Human terrain example 9 11 hijacker network

“Human Terrain” Example: 9/11 Hijacker Network


Iraq elections

Iraq Elections

Barzani Khamenei


Demonstration data set jihadist texts

Demonstration Data Set:Jihadist Texts

  • Approx. 250 translated texts

    • MEMRI

    • FBIS

    • Other Sources

  • 15 Authors

    • More than 1 text

    • Not well known


Simple text analysis the plagiarism check

Simple Text Analysis: The Plagiarism Check

Problem

  • Word matching is overly simple.

  • Ignores context

  • Actors can be overly weighted by writing more


Alternative themed analysis

Alternative: Themed Analysis

  • Traditional Network Analysis Methods

    • Citation Analysis

    • Physical Network

    • Communication or Financial Network

  • Themed Analysis

    • Relates nodes across multiple fields

      • One similar theme versus many similar themes


Demonstration text analysis

Demonstration: Text Analysis


Theme scores

Theme Scores

  • Problem

    • Commander needs information in representations he/she understands.

    • Networks can compare authors across single themes

    • But difficult to compare authors across multiple themes

*Theme Score is the sum of each word’s score per text


Constructing a network across multiple themes

Constructing a Network Across Multiple Themes

  • Scrub Texts

  • Construct Theme Scores

  • Construct Confidence Intervals

  • Discern Similarity between Nodes

    • Binary or Standardized Difference of Means

  • Create Square Matrix

  • Draw Network

*why not ANOVA?


Confidence intervals

Confidence Intervals

  • 95% Confidence Interval =

    • Each Author, Each Theme

  • Example:


Relationship scores

Relationship Scores

  • Each possible pair of authors per theme

    • Overlapping Confidence Intervals

    • Disparate Confidence Intervals


Matrix construction

Matrix Construction

  • Multiplication of Scores for each author and each theme

Geometric Mean =

  • Resultant Square Matrix


Themed network

Themed Network


Theme analysis confidence interval vs average

Theme Analysis: Confidence Interval vs Average

  • Able to look at each theme individually.

  • Average Rank does not account for connections importance, weighting, predictors

  • Themes are combined

  • Can see connections between authors across a combination of themes.


Method comparison

Method Comparison


Conclusions

Conclusions

  • Socially Engineered Algorithms involve extensive tradeoffs and decisions by the mathematician that can significantly impact commander’s decision-making.

  • Multiple views of the same data is a critical requirement.

  • Find Linkages in large amounts of data

  • Find Connections across multiple fields

  • Non-Tangible Relationships

  • Real World: Track / Catch criminals / radical ideologues

  • Representation of Human Terrain


Future work

Future Work

  • Publish method in Journal of Computational and Mathematical Organization Theory

  • Integration into ORA (Organizational Risk Analysis) Statistical Software: In use by Intelligence Analysts.

  • Analysis of change over time


Questions

Questions?


References

References

  • Dr. Jaret Brachman. Combating Terrorism Center, USMA.

  • Dr. Steven Corman. Hugh Downs School of Human Communication, Arizona State University.

  • http://www.checkpoint-online.ch/CheckPoint/Images/N-HusseinCapture.jpg

  • http://www.salmac.co.za/profile-writing-arabic.gif

  • Wasserman, Stanley and Katherine Faust. Social Network Analysis: Methods and Applications. New York: Cambridge University Press, 1994, 4.


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