Research Article Detection and Classification of Psychopathic Personality Trait from Social Media Text Using Deep Learning Model Junaid Asghar, 1 Saima Akbar, 2 Muhammad Zubair Asghar , 2 Bashir Ahmad, 3 Mabrook S. Al-Rakhami , 4 and Abdu Gumaei 4,5 1 Faculty of Pharmacy, Gomal University, D.I. Khan (KP), Pakistan 2 Institute of Computing and Information Technology, Gomal University, D.I. Khan (KP), Pakistan 3 Dept. of Computer Science, Qurtaba University, D.I. Khan (KP), Pakistan 4 Research Chair of Pervasive and Mobile Computing; Information Systems Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia 5 Computer Science Department, Faculty of Applied Sciences, Taiz University, Taiz 6803, Yemen Correspondence should be addressed to Mabrook S. Al-Rakhami; malrakhami@ksu.edu.sa Received 20 February 2021; Revised 20 March 2021; Accepted 26 March 2021; Published 10 April 2021 Academic Editor: Waqas Haider Bangyal Copyright © 2021 Junaid Asghar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Nowadays, there is a digital era, where social media sites like Facebook, Google, Twitter, and YouTube are used by the majority of people, generating a lot of textual content. The user-generated textual content discloses important information about people’s personalities, identifying a special type of people known as psychopaths. The aim of this work is to classify the input text into psychopath and nonpsychopath traits. Most of the existing work on psychopath’s detection has been performed in the psychology domain using traditional approaches, like SRPIII technique with limited dataset size. Therefore, it motivates us to build an advanced computational model for psychopath’s detection in the text analytics domain. In this work, we investigate an advanced deep learning technique, namely, attention-based BILSTM for psychopath’s detection with an increased dataset size for efficient classification of the input text into psychopath vs. nonpsychopath classes. 1. Introduction According to psychology, traits provide a way of describing a person, such as generous, out-going, and short-tempered. The trait-driven approach is the most focused area in psychology lit- erature. Trait depicts a person’s characteristic to make a response and reaction to a certain situation in a specific way [1]. Perceive them as being cunning, antisocial, and manipu- lative, and such individuals merely exist about 1% of the pop- ulation [2]. Individuals with psychopathic behavior usually feel no discomfort or guilt while making immoral choices and displaying immoral behavior and committing immoral actions. Hervey Cleckley was the first person who officially coined the concept of psychopathy in 1964 by identifying a group of his patients lacking morality. The modern ideology is about psychopath actions [2]. In connection to the dark triad, psychopathy is termed as the inherent feature of dark triad person to show no regrets for displaying immoral and harmful behavior, because they lack empathy and guilt, while committing wrong and sinful and wicked deeds. Medical sci- ence declares such persons as being mentally disordered and harmful to both themselves and society with a high tendency of instability [3]. In addition to their antisocial and criminal behavior, such individuals have certain distinctive features, such as lack of realistic goals, parasitic lifestyle, and lack of responsibility. They are incapable of learning from their experiences and cannot establish fruitful relationships. Being emotionally immature, such individuals are not affected by punishments and continue with their antisocial behavior [4]. 1.1. Research Study Motivation. The traditional techniques of identifying psychopaths include the following: Psychopathy Checklist-Revised, PCL-R, Welsh Anxiety Scale [5], PCL-R, Wmatrix linguistic analysis, Dictionary of Affect and Hindawi Computational and Mathematical Methods in Medicine Volume 2021, Article ID 5512241, 10 pages https://doi.org/10.1155/2021/5512241