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A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis
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Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)-Based Automatic

José Alberto Benítez-Andrades1, José-Manuel Alija-Pérez2, Maria-Esther Vidal3

  • 1SALBIS Research Group, Department of Electric, Systems and Automatics Engineering, University of León, León, Spain.

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PubMed
Summary
This summary is machine-generated.

Machine learning models, particularly transformer-based ones, can effectively categorize tweets about eating disorders. Bidirectional encoder representations from transformer models show superior performance in classifying this sensitive online content.

Keywords:
BERTNLPTwitterbidirectional encoder representations from transformerclassificationdatadeep learningdietdisordereating disordermachine learningmental healthmodelnatural language processingnutritionperformancesocial mediaweight

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Area of Science:

  • Computational linguistics
  • Machine learning applications in mental health
  • Social media analysis

Background:

  • Eating disorders represent a growing public health concern.
  • Social media platforms are significant sources of information and discussion regarding eating disorders.
  • Understanding online discourse is crucial for intervention and support.

Purpose of the Study:

  • To identify machine learning models for efficient categorization of eating disorder-related tweets.
  • To evaluate the performance of various machine learning and deep learning models for tweet classification.
  • To assess the utility of automated tweet analysis in the eating disorder domain.

Main Methods:

  • Collected over one million tweets related to eating disorders over three months.
  • Preprocessed and labeled a subset of 2000 tweets across four categories: patient-authored, pro-eating disorder, informative, and scientific.
  • Applied and evaluated traditional machine learning and deep learning models, including transformer-based approaches, using accuracy, F1 score, and computational time.

Main Results:

  • Transformer-based models, specifically bidirectional encoder representations, achieved the highest F1 scores (71.1%-86.4%) across all four categorization tasks.
  • These advanced deep learning models outperformed traditional machine learning techniques in classifying eating disorder tweets.
  • Significant computational resources are required for these high-performing models.

Conclusions:

  • Bidirectional encoder representations from transformer models demonstrate superior efficacy in classifying eating disorder-related tweets.
  • While computationally intensive, these advanced models offer enhanced accuracy for analyzing online discourse on eating disorders.
  • The findings support the use of sophisticated machine learning for understanding and potentially intervening in online discussions about eating disorders.