This paper presents a comprehensive study of the relationship between personality, gender, and age in social media language using an open-vocabulary approach. The authors analyzed 700 million words, phrases, and topics from 75,000 Facebook users who also completed standard personality tests. Their method, called Differential Language Analysis (DLA), automatically extracts linguistic features from the text, including words, phrases, and topics, and correlates them with demographic and psychological attributes. The study found significant variations in language use across different personality traits, genders, and ages, providing new insights into psychosocial processes. For example, extraverts mentioned social activities more, while introverts showed an interest in Japanese media. The open-vocabulary approach also yielded more accurate predictive models for gender, age, and personality compared to traditional closed-vocabulary methods. The authors' findings suggest that open-vocabulary analyses can provide deeper insights into the behavioral and psychological characteristics of individuals, and they offer a detailed dataset for future research.This paper presents a comprehensive study of the relationship between personality, gender, and age in social media language using an open-vocabulary approach. The authors analyzed 700 million words, phrases, and topics from 75,000 Facebook users who also completed standard personality tests. Their method, called Differential Language Analysis (DLA), automatically extracts linguistic features from the text, including words, phrases, and topics, and correlates them with demographic and psychological attributes. The study found significant variations in language use across different personality traits, genders, and ages, providing new insights into psychosocial processes. For example, extraverts mentioned social activities more, while introverts showed an interest in Japanese media. The open-vocabulary approach also yielded more accurate predictive models for gender, age, and personality compared to traditional closed-vocabulary methods. The authors' findings suggest that open-vocabulary analyses can provide deeper insights into the behavioral and psychological characteristics of individuals, and they offer a detailed dataset for future research.