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Predicting MBTI personality type of Twitter users
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Li, Weiling. Predicting MBTI personality type of Twitter users. Retrieved from https://doi.org/doi:10.7282/t3-75wc-2x18

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TitlePredicting MBTI personality type of Twitter users
NameLi, Weiling (author); Hart, Daniel (chair); Allred, Sarah R. (internal member); Chiu, I-Ming (internal member); Rutgers University; Camden Graduate School
Date Created2021
Other Date2021-05 (degree)
SubjectMBTI, Psychology
Extent1 online resource (ii, 34 pages)
DescriptionAs the internet becomes more pervasive, people shift their self-expression and social interaction activities towards online platforms. It is therefore relevant to relate personality traits to behavior in social media domains. In this study, I investigated the relationship between one’s social media profiles and their MBTI personalities. I performed this research based on 4000 Twitter users who self-report their personalities and 425,752 tweets these users posted. I extracted 26 features that map into five categories of the user’s social media activities: Social Connections, Social Activities, Content Topic, Content Sentiment, and Temporal Patterns of Activities. I used two-sample t-tests and stepwise logistic regressions to examine the association between people’s social media activities and their personality traits. Then I used machine learning methods to predict one’s personality based on their social media profiles. Findings indicate that: 1) there are strong associations between personality and social media activities; 2) MBTI personality could be predicted by social media data and the best model achieved an average testing accuracy of 67.6%. I discuss the implications for further research in broader domains.
NoteM.A.
NoteIncludes bibliographical references
Genretheses, ETD graduate
Persistent URLhttps://doi.org/doi:10.7282/t3-75wc-2x18
LanguageEnglish
CollectionCamden Graduate School Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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