@article{THESIS,
      recid = {3766},
      author = {Kadambi, Partha},
      title = {Exploring Personality and Online Social Engagement: An  Investigation of MBTI Users on Twitter},
      publisher = {University of Chicago},
      school = {M.A.},
      address = {2022-06},
      number = {THESIS},
      abstract = {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. },
      url = {http://knowledge.uchicago.edu/record/3766},
      doi = {https://doi.org/10.6082/uchicago.3766},
}