Call for Papers - Exploring the Dark Side of Human Personality: Beyond Social Networks with Artificial Intelligence and Big Data Analytics


Assessing human personality is a long-held notion that attempts to forecast the behavior and nature of humans using various factors. Big data analytics and artificial intelligence never offered before advantage in understanding the human personality and went beyond detecting/recognizing potential dark side which expandable. The growth of social networking platforms has witnessed a sudden surge in their usage since the last decade. People communicate, express their emotions, and share valuable thoughts in social networks, forming a large personality analytics repertoire by machine learning algorithms. Text analytics rooted with advanced syntax tagging represented a difference in world usage of psychopaths when compared to normal personalities. Automated feature extraction studies and relationship analytics on social network platforms identified the persons with Machiavellianism's distinct phenotypes. Image processing algorithms with advanced feature extraction modules potentially reveal the narcissistic behavior of people and help in behavior modeling to recover from promiscuous behavior. Social network analytics endowed with a psychopathy checklist (PCL) outstandingly measures the existence of psychopath behaviors like pathological lying, glib speech, grandiose self-worth, antisocial behavior, lack of empathy, along persons with parasitic lifestyles. Moreover, they can easily correlate the demographic parameters and identify potential micro and macro factors that are classified susceptible populations for future sustainability. Integrated linguistic-sentiment analytics identifies psychopathic killers' spoken/text language's sentiment, emotions, and grammatical structure of psychopathic killers' spoken/text language from the rest of the population. It precisely forecasts their antisocial behavior, improving smart cities' safety levels. Big data-enabled artificial intelligence is a promising tool in identifying antisocial and pro-peril personalities, ensuring safety in the living environment.

Big data analytics and artificial intelligence are a productive addition to social network analytics and help mitigate many crime occurrences. However, potential challenges in forecasting dark human personalities through social network platforms need to be analyzed for global adoption and recommendation. Precise assessment of dark human personalities is extremely challenging, and any misleading leads to disastrous side effect which needs to be considered. In many cases, assessment of antisocial psychology from social network analytics lack context, which reduces the reliability of the experiment. The dynamicity of the human personality demands assessment of person psychology very frequently to produce results of reliable accuracy, which is highly challenging with the limitations of social network analytics. Conformational bias in personality assessment depends on even subtle changes in the behavior, which is hard to assess through social network analytics and requires innovative strategies. On substantial improvement, social network analytics endowed with big data analytics and artificial intelligence would be promising to identify dark human personalities and help in establishing a serene, sustainable environment.

Therefore this special issue aims to discuss and highlight various aspects of machine learning algorithms, artificial intelligence, text analytics, stress disorders, human relationship mapping, cognitive behavior analytics, sentiment evaluation, antisocial behavior, and much more. We invite researchers from various fields to present their research articles, reviews, case studies, short communications, and perspectives in Exploring the Dark Side of Human Personality: Beyond Social Networks with Artificial Intelligence and Big Data Analytics.

Topics of Interest

  • Advances in social network analytics for D-factor analysis in behavior modeling
  • Research in the social recommendation and community detection in development of antisocial values
  • Trends in link prediction for identification of potential psychopaths for cognitive analytics
  • Insights in influence analysis of social network analysis for prediction of parasitic behavior
  • Advanced opinion dynamics and social behavior analysis for monitoring narcissism and psychopathy
  • Novel architectures for scalable social analytics in high dimensional spaces for detection of the susceptible population
  • Trends in large scale graph algorithms for forecasting the spread of antisocial thoughts
  • Advances in antisocial aversion monitoring and modelization for the sustainability of smart cities
  • Strategies to neutralize misinformation and disinformation to increase the reliability of social network analytics
  • Frontiers in automated generation of deceitful content for the protective influence of psychopaths for crime prevention
  • Prediction and evaluation of non-real-world events to mitigate the reduction in reliability through conformational biases.
  • Impact of drug abuse, alcoholism in development of dark human personality
  • Advanced model prediction to assess classification and clustering of population personality in high dimensional spaces


Proposed Special Issue Dates
Manuscript submission deadline: March 24, 2023
Authors notification: June 21, 2023
Revised papers due: August 22, 2023
Final notification: November 24, 2023

Guest Editors
Mahyudin Ritonga, Muhammadiyah University, West Sumatra, Indonesia
Miftcahul Huda, Universiti Pendidikan Sultan Idris, Perak, Malaysia
Martin Kustati, Universitas Islam Negeri Imam Bonjol Padang, Indonesia