On SAGE Insight: Circadian mood variations in Twitter content

From Brain and Neuroscience Advances

We reveal patterns of circadian variations in our collective emotions from the analysis of 800M anonymous microposts collected on Twitter across the United Kingdom. We use methods of Fourier analysis and statistical analysis to investigate periodic events in the use of words denoting a positive emotion, a negative emotion, or fatigue. Our research reveals two strong patterns: while the positive emotions show a bi-phasic behavior, peaking once in the morning and once in the night, the negative emotions reach their minima in the morning period, they gradually increase during the day to reach a peak late at night. Our research confirms previous results reported on diurnal mood variations and shows finer aspects of mood variations: sadness and the positive emotions are found to vary in response to the change of activity between the weekend and the weekdays, these two emotions are also found to vary between the seasons, showing interaction with the onset of sunlight exposure. The variations of anger and fatigue remain more stable in response to these changes in the external environment.

In the context of hormonal changes that occur during the day, anger shows a pattern that inversely mirrors the known circadian variation of plasma cortisol concentrations. While our study design prevents us from establishing a causal link between hormones and emotions, our research makes apparent interesting hormonal correlates such as cortisol. New research will be needed to investigate further, and separate the effect of other potential clues such as sleep pressure building up during the day, or diurnal changes in our social environment.

To the best of our knowledge this study is the largest of its kind, the patterns of circadian variations were sampled across the 54 largest cities in the United Kingdom and averaged over the course of four years. We reveal accurate patterns of variation for meal times, as well as other natural zeitgebers related to seasonal changes such as light or temperature. These methods of sampling and statistical analysis that we demonstrate on the social media provide valuable tools for the study of our emotions and for the understanding of their interaction within the circadian rhythm. We hope this study will encourage other researchers to use the social media to help in our understanding of the brain and mental health disorders.

By Fabon Dzogang 

Image result for Fabon Dzogang

Abstract

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 utilized 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.

Article details

Circadian mood variations in Twitter content
Fabon Dzogang, Stafford Lightman, Nello Cristianini
First Published December 1, 2017 
DOI: 10.1177/2398212817744501
From Brain and Neuroscience Advances

 
     
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