A large scale of investigation had been carried out to predict the personality, or in precise, the behaviour of online users through user-generated texts, such as Tweets and status messages. Nevertheless, only a handful of machine learning (ML) studies have applied the personality model to assess criminality behaviour, particularly within the context of Malay social network messages. Based on the concept of sentiment valence, this study annotated a list of Malay Tweets that might be subjected to crime or illicit messages from the stance of Psychoticism trait. Consequently, the supervised-based text classification method was conducted by using Naïve Bayes (NB), Sequential Minimal Optimisation (SMO), and Decision Tree (DT) on Tweets using several features determined via Chi Square (x2). The analyses outcomes signified that SMO outperformed other classifiers insignificantly by achieving 92.85% of accuracy. Based on x2, several swear terms, such as bontot, melancap, and kote, displayed significant correlation with Psychoticism Tweets due to the nature of the trait that has been subjected to criminality behaviour, for instance, aggressive and antisocial attributes. The findings illustrate the possibilities to adapt several personality aspects in order to enhance the effectiveness in detecting illicit social network messages.
Universiti Sains Malaysia has started the Big Brain Data Initiative project since the last two years as brain mapping techniques have proven to be important in understanding the molecular, cellular and functional mechanisms of the brain. This Big Brain Data Initiative can be a platform for neurophysicians and neurosurgeons, psychiatrists, psychologists, cognitive neuroscientists, neurotechnologists and other researchers to improve brain mapping techniques. Data collection from a cohort of multiracial population in Malaysia is important for present and future research and finding cure for neurological and mental illness. Malaysia is one of the participant of the Global Brain Consortium (GBC) supported by the World Health Organization. This project is a part of its contribution via the third GBC goal which is influencing the policy process within and between high-income countries and low- and middle-income countries, such as pathways for fair data-sharing of multi-modal imaging data, starting with electroencephalographic data.