Identifying Individual Differences in Gender, Ethnicity, and Personality from Dialogue for Deception Detection

By Sarah Ita Levitan, Yocheved Levitan, Guozhen An, Michelle Levine, Rivka Levitan, Andrew Rosenberg, Julia Hirschberg. 2016

26 citations recorded by the source corpus.

When automatically detecting deception, it is important to model individual differences across speakers. We explore the automatic identification of individual traits such as gender, native language, and personality, using acoustic-prosodic and lexical features from an initial non-deceptive dialogue. We also explore predicting success at deception and at deception detection, using the same features

Subfields: Clinical Psychology, Information Systems, Social Psychology, Sentiment & Opinion Analysis, AI Ethics, Fairness & Society

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Clinical Psychology

1. Alexander Gelbukh
2. Navonil Majumder
3. Yash Mehta
4. Erik Cambria

In Information Systems

1. Alexander Gelbukh
2. Erik Cambria

In Social Psychology

1. Navonil Majumder
2. Alexander Gelbukh
3. Yash Mehta
4. Erik Cambria

In Sentiment & Opinion Analysis

1. Navonil Majumder
2. Erik Cambria

Citations in the corpus

• Recent trends in deep learning based personality detection (2019, methodology): “84 [98] CMU self recorded database [79] Big 5 [79] Columbia deception corpus [58] [59] Visual Face Evaluation [69] [86] YouTube Vlogs Big 5 0.”