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Circadian mood variations in Twitter content.

Fabon Dzogang1, Stafford Lightman2, Nello Cristianini1

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Brain and Neuroscience Advances
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Summary

Social media data reveals distinct daily mood patterns, with anger and fatigue showing stable circadian rhythms. This analysis offers new insights into understanding mood disorders through collective emotional expression.

Keywords:
Circadian rhythms and sleepcomputational neuroscienceemotionhuman behaviour

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Area of Science:

  • Chronobiology
  • Computational Social Science
  • Affective Science

Background:

  • Circadian rhythms centrally regulate sleep, cognition, and metabolism, influenced by environmental cues.
  • Understanding mood's circadian regulation is crucial for daily functioning, traditionally relying on subjective self-reports.
  • Large-scale data analysis is essential for a comprehensive understanding of mood's daily cycles.

Purpose of the Study:

  • To investigate circadian patterns in collective mood and fatigue using a large-scale social media dataset.
  • To identify daily, seasonal, and weekly variations in emotional expression and fatigue.
  • To explore the relationship between social media-derived mood patterns and known biological rhythms.

Main Methods:

  • Analysis of over 800 million Twitter messages collected over four years in the UK.
  • Statistical and Fourier analyses to detect periodic structures, extrema, and change-points in emotional expression and fatigue.
  • Comparison of temporal stability of mood signals across seasons and weekend/weekday divides.

Main Results:

  • Distinct circadian patterns were identified for positive and negative moods.
  • Fatigue and anger exhibited stable cycles irrespective of season or day of the week.
  • Anger and fatigue patterns inversely correlated with plasma cortisol concentrations, with a morning inflection point observed for most metrics.

Conclusions:

  • Social media analysis presents a valuable, large-scale resource for studying mental disorders.
  • Observed circadian and mood patterns in social media data may inform our understanding of mood disorder pathologies.
  • The findings highlight the potential of digital footprints in advancing affective and chronobiological research.