机构:
Georgia Tech Res Inst, Atlanta, GA 30332 USAGeorgia Tech Res Inst, Atlanta, GA 30332 USA
Briscoe, Erica J.
[1
]
Appling, D. Scott
论文数: 0引用数: 0
h-index: 0
机构:
Georgia Tech Res Inst, Atlanta, GA 30332 USAGeorgia Tech Res Inst, Atlanta, GA 30332 USA
Appling, D. Scott
[1
]
Hayes, Heather
论文数: 0引用数: 0
h-index: 0
机构:
Georgia Tech Res Inst, Atlanta, GA 30332 USAGeorgia Tech Res Inst, Atlanta, GA 30332 USA
Hayes, Heather
[1
]
机构:
[1] Georgia Tech Res Inst, Atlanta, GA 30332 USA
来源:
2014 47TH HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES (HICSS)
|
2014年
关键词:
DETECTING DECEPTION;
D O I:
10.1109/HICSS.2014.186
中图分类号:
TP [自动化技术、计算机技术];
学科分类号:
0812 ;
摘要:
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.