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Area of Science:

  • Computational Social Science
  • Psychological Informatics
  • Natural Language Processing

Background:

  • Personality assessment traditionally relies on self-report questionnaires.
  • Emerging computational methods offer alternative approaches to personality analysis.

Purpose of the Study:

  • To develop and apply a machine learning classifier for predicting Myers-Briggs Type Indicator (MBTI) personality types.
  • To analyze the personality types of YouTube users commenting on specific content genres.

Main Methods:

  • Utilized Natural Language Processing (NLP) and Machine Learning (ML) to build a personality classifier.
  • Extracted text samples from YouTube comments for analysis.
  • Classified users into MBTI types based on comment content.

Main Results:

  • Accurate personality type estimation is achievable with approximately 100 topic-relevant words per user.
  • INFP emerged as the most prevalent MBTI type among analyzed YouTubers.
  • INTP was the predominant type for conspiracy theory comments, while INFP dominated spiritual and travel content discussions.

Conclusions:

  • Computational analysis of social media text provides a viable method for personality type assessment.
  • Introverted and Intuitive (N) personality traits are significantly represented in online commentary.
  • Content topic influences the distribution of personality types observed in online user engagement.