Text-based personality analysis by computational models is an emerging field with the potential to significantly improve on key weaknesses of survey-based personality assessment. We1 investigate 3848 profiles from Twitter with self-labeled Myers-Briggs personality types (MBTI) – a framework closely related to the Five Factor Model of personality – to better understand how text-based digital traces from social media can be used to predict user personality traits and explore relationships between community discourse and personality traits. We leverage machine learning and network analysis approaches to analyze relationships between behavioral traits and various facets of engagement on social media. We find that both social engagement metadata and multiple modalities of text from social profiles significantly correlate with factors of the MBTI system – especially the Intuitive/Sensing trait that corresponds to the dimension of Openness in the Five Factor model of personality (the Big 5). We discuss our findings and their implications for the validity of the MBTI and the lexical hypothesis, a foundational theory underlying the Five Factor Model of personality that links language use and behavior. Our results hold optimistic implications for personality psychologists, computational linguists, and other social scientists seeking to clarify the role of behavioral dimensions underlying semi-structured text-based social engagement and illuminate the links between sociolinguistics and core dispositional traits.