Title

Applying Deep Neural Networks for Predicting Dark Triad Personality Trait of Online Users

Document Type

Conference Proceeding

Source of Publication

International Conference on Information Networking

Publication Date

1-1-2020

Abstract

© 2020 IEEE. In the recent times, the social networking sites act as a rich source of information, which is shared among online users, who post comments and express their opinions in the form of likes and dislikes. Such content reflects important clues about the personality and behavior of the online community. The dark triad personality traits, such as the psychopathic behavior of individuals, can be detected using computational models. The earlier studies on the dark triad (psychopath) prediction exploit traditional machine learning techniques with limited dataset size. Therefore, it is required to develop an advanced deep neural network-based technique. In this work, we implement a deep neural network model, namely BILSTM for the efficient prediction of dark triad (psychopath) personality traits regarding online users. Experimental results depict that the proposed model attained an improved AUC (0.82) when compared to the baseline study.

ISBN

9781728141985

ISSN

1976-7684

Publisher

IEEE Computer Society

Volume

2020-January

First Page

102

Last Page

105

Disciplines

Computer Sciences

Keywords

BILSTM, dark triad, light triad, machine learning, personality prediction

Scopus ID

85082111224

Indexed in Scopus

yes

Open Access

no

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