Applying Deep Neural Networks for Predicting Dark Triad Personality Trait of Online Users
Source of Publication
International Conference on Information Networking
© 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.
IEEE Computer Society
BILSTM, dark triad, light triad, machine learning, personality prediction
Ahmad, Hussain; Arif, Areeba; Khattak, Asad Masood; Habib, Anam; Asghar, Muhammad Zubair; and Shah, Babar, "Applying Deep Neural Networks for Predicting Dark Triad Personality Trait of Online Users" (2020). All Works. 524.
Indexed in Scopus