Applying Deep Machine Learning for psycho-demographic profiling of Internet users using O.C.E.A.N. model of personality

In the modern era, each Internet user leaves enormous amounts of auxiliary digital residuals (footprints) by using a variety of on-line services. All this data is already collected and stored for many years. In recent works, it was demonstrated that it's possible to apply simple machine learning methods to analyze collected digital footprints and to create psycho-demographic profiles of individuals. However, while these works clearly demonstrated the applicability of machine learning methods for such an analysis, created simple prediction models still lacks accuracy necessary to be successfully applied for practical needs. We have assumed that using advanced deep machine learning methods may considerably increase the accuracy of predictions. We started with simple machine learning methods to estimate basic prediction performance and moved further by applying advanced methods based on shallow and deep neural networks. Then we compared prediction power of studied models and made conclusions about its performance. Finally, we made hypotheses how prediction accuracy can be further improved. As result of this work, we provide full source code used in the experiments for all interested researchers and practitioners in corresponding GitHub repository. We believe that applying deep machine learning for psycho-demographic profiling may have an enormous impact on the society (for good or worse) and provides means for Artificial Intelligence (AI) systems to better understand humans by creating their psychological profiles. Thus AI agents may achieve the human-like ability to participate in conversation (communication) flow by anticipating human opponents' reactions, expectations, and behavior

Psychological targeting as an effective approach to digital mass persuasion

People are exposed to persuasive communication across many different contexts: governments, companies, and political parties use persuasive appeals to encourage people to eat healthier, purchase a particular product, or vote for a specific candidate. Laboratory studies show that such persuasive appeals are more effective in influencing behavior when they are tailored to individuals’ unique psychological characteristics. Yet, the investigation of large-scale psychological persuasion in the real world has been hindered by the questionnaire-based nature of psychological assessment. Recent research, however, shows that people’s psychological characteristics can be accurately predicted from their digital footprints, such as their Facebook Likes or Tweets. Capitalizing on this new form of psychological assessment from digital footprints, we test the effects of psychological persuasion on people’s actual behavior in an ecologically valid setting. In three field experiments that reached over 3.5 million individuals with psychologically-tailored advertising, we find that matching the content of persuasive appeals to individuals’ psychological characteristics significantly altered their behavior as measured by clicks and purchases. Persuasive appeals that were matched to people’s extraversion or openness-to-experience level resulted in up to 40% more clicks and up to 50% more purchases than their mismatching or un-personalized counterparts. Our findings suggest that the application of psychological targeting makes it possible to influence the behavior of large groups of people by tailoring persuasive appeals to the psychological needs of the target audiences. We discuss both the potential benefits of this method for helping individuals make better decisions and the potential pitfalls related to manipulation and privacy

The Holistic Archival Personality Profiling Model (HAPPM): Comprehensive Data Integration for Personality Analysis

The traditional approach to biographical profiling, predominantly reliant on limited and fragmented datasets, has frequently resulted in superficial personality understandings. This is largely due to an overemphasis on official records and notable events, neglecting the rich tapestry of everyday experiences and personal interactions that significantly shape personalities. To address this shortcoming, this article introduces a multi-disciplinary methodology, The Holistic Archival Personality Profiling Model (HAPPM), which integrates a diverse array of archival materials, including personal correspondences, social media footprints, and family memorabilia. This approach involves digitizing various data forms, including handwritten documents, into machine-readable text, and then semantically classifying this data with biotags, chronotags, and geotags for organization within specific spatial and temporal contexts. Such comprehensive data aggregation establishes a more accurate space-time continuum for individuals, enhancing our understanding of their lives. The innovative aspect of HAPPM is the utilization of large language models to converse with the data, facilitating a more holistic representation of personalities. Preliminary results from applying HAPPM have shown its efficacy in uncovering previously unknown aspects of individual lives, offering insights into personal beliefs, daily routines, and social interactions. This has been validated through comparative analysis with existing biographical data, revealing a more complete and nuanced understanding of personalities. Therefore, HAPPM marks a significant advancement in personality profiling, capturing not only the grandiose but also the mundane, and offering a comprehensive tool for researchers and historians to explore the full spectrum of human experience

New Talent Signals: Shiny New Objects or a Brave New World?

Almost 20 years after McKinsey introduced the idea of a war for talent, technology is disrupting the talent identification industry. From smartphone profiling apps to workplace big data, the digital revolution has produced a wide range of new tools for making quick and cheap inferences about human potential and predicting future work performance. However, academic industrial–organizational (I-O) psychologists appear to be mostly spectators. Indeed, there is little scientific research on innovative assessment methods, leaving human resources (HR) practitioners with no credible evidence to evaluate the utility of such tools. To this end, this article provides an overview of new talent identification tools, using traditional workplace assessment methods as the organizing framework for classifying and evaluating new tools, which are largely technologically enhanced versions of traditional methods. We highlight some opportunities and challenges for I-O psychology practitioners interested in exploring and improving these innovations