Data from: Circadian mood variations in Twitter content

Background: Circadian regulation of sleep, cognition, and metabolic state
is driven by a central clock, which is in turn entrained by environmental
signals. Understanding the circadian regulation of mood, which is vital
for coping with day-to-day needs, requires large datasets and has
classically utilised subjective reporting. Methods: In this study, we use
a massive dataset of over 800 million Twitter messages collected over 4
years in the United Kingdom. We extract robust signals of the changes that
happened during the course of the day in the collective expression of
emotions and fatigue. We use methods of statistical analysis and Fourier
analysis to identify periodic structures, extrema, change-points, and
compare the stability of these events across seasons and weekends.
Results: We reveal strong, but different, circadian patterns for positive
and negative moods. The cycles of fatigue and anger appear remarkably
stable across seasons and weekend/weekday boundaries. Positive mood and
sadness interact more in response to these changing conditions. Anger and,
to a lower extent, fatigue show a pattern that inversely mirrors the known
circadian variation of plasma cortisol concentrations. Most quantities
show a strong inflexion in the morning. Conclusion: Since circadian rhythm
and sleep disorders have been reported across the whole spectrum of mood
disorders, we suggest that analysis of social media could provide a
valuable resource to the understanding of mental disorder.