Saint or Sinner? Language-Action Cues for Modeling Deception Using Support Vector Machines

  • Shuyuan Mary Ho
  • Xiuwen Liu
  • Cheryl Booth
  • Aravind Hariharan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9708)

Abstract

In text-based online communication, the clues available to the communicator for ascertaining the underlying intent of a message sender and discerning whether a message is deceptive are often limited to the text. Nonetheless, research has shown that it is possible to detect deception with reasonable accuracy by applying certain classification methodologies to certain observable language-action cues. This paper explores the viability of adopting support vector machines (SVMs) to develop an automated process for deception detection in computer-mediated communications (CMC). In particular, it examines the prediction accuracy of SVM models with different kernel functions on data collected from a controlled online interactive game set up on a Google + Hangout platform. The results indicate that SVM models using the radial basis function (RBF) kernel can classify the complex relationships with high accuracy between language-action cues and deception.

Notes

Acknowledgement

The authors wish to thank the National Science Foundation EAGER grants #1347113, 09/01/13—08/31/15, the Florida Center for Cybersecurity Collaborative Seed Grant 03/01/15—02/28/16, and the Florida State University Council for Research and Creativity Planning Grant #034138, 12/01/13—12/12/14.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Shuyuan Mary Ho
    • 1
  • Xiuwen Liu
    • 1
  • Cheryl Booth
    • 1
  • Aravind Hariharan
    • 1
  1. 1.Florida State UniversityTallahasseeUSA

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