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Using Novel Social Network Visualization Techniques to Aid in the Detection and Analysis of Deception in Internal Intelligence Data

Identifying elements of deception in utilizing social network visualization techniques is an under-researched topic that can vastly assist intelligence, national security, and law enforcement agencies. The ability to use social network analysis (SNA) metrics and techniques to identify deception will increase analytic efficiency, improve operations and protect lives. This work seeks to extend previous social network analysis research by Magnus Sköld and Christopher Yang et al. Our goal is to assess whether deception is detectable using traditional SNA methods. In part, this research seeks to answer the question: Can novel visualization techniques aid in the detection and analysis of deception in a social environment? Data derived from the SYNCOIN dataset (Graham, 2012) will serve as the foundation for this work. After translating the SYNCOIN data into its representative social network visualization, traditional SNA methods and techniques will be assessed for their ability to detect the various elements of deception that we know exist within the scenario. This research will be considered successful if we are able to use traditional SNA techniques, or develop novel techniques and metrics to accurately determine the elements of deception within the SYNCOIN data. This study will be exploratory in nature and will be primarily quantitative with qualitative interpretation of the results.

Project Team

  • Principal Investigator: Tyler Yazujian, Undergraduate Student, Security and Risk Analysis, tjy5041@psu.edu
  • Co-Investigators: Glenn Sterner, Post-Doctoral Scholar, Justice Center for Research, Department of Sociology and Criminology, ges5098@psu.edu; Peter Forster, Associate Dean for Online and Professional Education, College of Information Sciences and Technolog, pforster@ist.psu.edu; Col. Jacob Graham, Professor of Practice of Information Sciences and Techonlogy, jgraham@ist.psu.edu

    About the Project

    • This project serves as the master’s project for Tyler Yazujian.

      Research Questions

      • To what extent can novel social network visualization techniques aid in the detection and analysis of deception in a high-risk security environment?

        Project Details

        This project focuses on the following objectives:

        1. Document the social networks of a terrorist network scenario and create a social network dataset.
        2. Assess the viability of social network visualization and analysis method to detect deception (tactical, operational, or strategic).
        3. Conduct an experiment to quantitatively describe how social network visualization can aid in deception detection.

          Implications

          This study will have important implications for utilizing social network analysis methodologies for detecting deception in intelligence gathering processes.  This will provide guidance on the use of this method within intelligence gathering, eliminating misinformation in intelligence, and assisting in keeping our military personnel and country more secure.

          Project Products

          • This will serve as the master’s project for Tyler Yazujian.
          • This project engages Tyler Yazujian as a dual undergraduate/graduate student.

          We anticipate a peer reviewed journal article to be submitted Fall 2016.