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A textual-based featuring approach for depression detection using machine learning classifiers and social media

Raymond Chiong1, Gregorius Satia Budhi2, Sandeep Dhakal1

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Machine learning can detect depression from social media posts, even without explicit keywords. This approach aids in identifying undiagnosed depression for timely treatment.

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Area of Science:

  • Computational linguistics
  • Psychiatry
  • Social media analytics

Background:

  • Depression is a leading cause of suicide, with many cases remaining undiagnosed and untreated.
  • Social media text analysis shows potential for identifying individuals with major depressive disorder.
  • Current methods may struggle with detecting depression when specific keywords are absent.

Purpose of the Study:

  • To evaluate the effectiveness of machine learning in detecting depression from social media posts.
  • To develop a generalized approach for depression detection using text analysis, independent of explicit keywords.
  • To assess model performance across diverse social media platforms.

Main Methods:

  • Utilized text preprocessing and feature extraction techniques.
  • Employed single and ensemble machine learning classifiers.
  • Trained and tested models on public Twitter datasets, then validated on Facebook, Reddit, and electronic diary data.

Main Results:

  • The proposed machine learning approach effectively detected depression in social media texts.
  • Successful detection was achieved even when training data lacked specific depression-related keywords.
  • Models demonstrated robust performance when tested on datasets from different social media sources.

Conclusions:

  • Machine learning offers a viable method for detecting depression through social media text analysis.
  • The approach is effective even without explicit mentions of 'depression' or 'diagnosis'.
  • This technology can aid in identifying and potentially treating undiagnosed depression in social media users.