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Exploring Abnormal Behavior Patterns of Online Users With Emotional Eating Behavior: Topic Modeling Study.

Youjin Hwang1, Hyung Jun Kim1, Hyung Jin Choi2

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Summary
This summary is machine-generated.

This study analyzed online emotional eating behaviors using machine learning and topic modeling. Findings reveal distinct user behavior patterns, paving the way for personalized support systems for emotional eaters.

Keywords:
Latent Dirichlet Allocationbehavior analysisdata-driven researcheating disorderemotional eatingmachine learningtopic modeling

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Area of Science:

  • Behavioral Science
  • Computational Linguistics
  • Data Science

Background:

  • Emotional eating (EE) is a significant symptom of eating disorders, yet behavioral data is scarce.
  • Understanding online EE behaviors is crucial for developing personalized support systems (PSS).

Purpose of the Study:

  • To analyze the behavior patterns of online emotional eaters.
  • To lay the groundwork for designing a personalized intervention system for EE.

Main Methods:

  • Utilized a machine learning (ML) framework and Latent Dirichlet Allocation (LDA) for topic modeling.
  • Analyzed 185,950 posts and 3,528,107 comments from Reddit's /r/loseit subcommunity (July 2014-May 2018).
  • Developed an ML classifier (90.64% accuracy) and expert-labeled data for refined EE behavior identification.

Main Results:

  • Identified four main topics of online EE behaviors: addressing feelings, sharing physical changes, dietary information, and dietary strategies.
  • Feedback topics included dietary information, compliments, consolation, bot feedback, and health information.
  • Feedback topic distribution varied significantly based on EE behavior type (P<.001).

Conclusions:

  • Introduced a data-driven approach to analyze online EE behavior patterns.
  • Demonstrated the utility of LDA topic modeling for studying abnormal behaviors in medical research.
  • Highlighted the potential of ML and topic modeling for categorizing text-based behavioral data in personalized medicine.