• DocumentCode
    264639
  • Title

    Cues to Deception in Social Media Communications

  • Author

    Briscoe, Erica J. ; Appling, D. Scott ; Hayes, Heather

  • Author_Institution
    Georgia Tech Res. Inst., Atlanta, GA, USA
  • fYear
    2014
  • fDate
    6-9 Jan. 2014
  • Firstpage
    1435
  • Lastpage
    1443
  • Abstract
    With the increasing reliance on social media as a dominant communication medium for current news and personal communications, communicators are capable of executing deception with relative ease. While past-related research has investigated written deception in traditional forms of computer mediated communication (e.g. email), we are interested determining if those same indicators hold in social media-like communication and if new, social-media specific linguistic cues to deception exist. Our contribution is two-fold: 1) we present results on human subjects experimentation to confirm existing and new linguistic cues to deception; 2) we present results on classifying deception from training machine learning classifiers using our best features to achieve an average 90% accuracy in cross fold validation.
  • Keywords
    learning (artificial intelligence); social networking (online); computer mediated communication; cross fold validation; current news; machine learning classifiers; personal communications; social media communications; written deception; Accuracy; Complexity theory; Electronic mail; Face; Media; Pragmatics; Social network services; computer mediated communication; deception; deception detection; deception generation; linguistic cues; online social networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    System Sciences (HICSS), 2014 47th Hawaii International Conference on
  • Conference_Location
    Waikoloa, HI
  • Type

    conf

  • DOI
    10.1109/HICSS.2014.186
  • Filename
    6758783