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Predicting Depression From Language-Based Emotion Dynamics: Longitudinal Analysis of Facebook and Twitter Status

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Area of Science:

  • Psychology
  • Computational Linguistics
  • Digital Health

Background:

  • Social media use is linked to depression, but previous metrics focused on averages, not emotional dynamics.
  • Emotional volatility, or fluctuations in expressed emotions, may offer new insights into mental health.
  • Existing research often overlooks the dynamic nature of emotional expression online.

Purpose of the Study:

  • To investigate the relationship between depression severity and the variability and instability of emotion word use on Facebook and Twitter.
  • To explore how time-structured (instability) and time-unstructured (variability) emotional expression correlates with depression.
  • To compare these associations across different social media platforms.

Main Methods:

  • Collected Facebook and Twitter status updates and depression severity ratings from 29 Facebook and 49 Twitter users via the MoodPrism app.
  • Computed average emotion word proportions, within-person variability, and instability of emotion word expression.
  • Analyzed associations between these linguistic measures and depression severity, controlling for other factors.

Main Results:

  • On Facebook, negative emotion word instability predicted higher depression severity, even after accounting for average use and variability.
  • Conversely, on Twitter, greater negative emotion word variability was associated with lower depression severity.
  • Explored differences in emotion word patterns and psychological traits between Facebook and Twitter users.

Conclusions:

  • Negative emotion word instability may serve as a sensitive, platform-dependent indicator for depression screening on social media.
  • The dynamic measurement of emotional expression offers a novel approach to understanding mental health online.
  • Platform-specific analysis is crucial when using social media data for mental health assessments.